Jurnal Tekno Insentif最新文献

{"title":"Evaluasi Sisa Material Konstruksi Menggunakan Metode Pareto Dan Fishbone Diagram Pada Proyek Pembangunan Ruko Kawanua Emerald City","authors":"Edwardo M. Tambuwun, Deane R. O. Walangitan, Idris Masuara","doi":"10.35793/jts.v22i88.55343","DOIUrl":"https://doi.org/10.35793/jts.v22i88.55343","url":null,"abstract":"Pada setiap pekerjaan konstruksi bangunan, tidak akan terlepas dari salah satu komponen utamanya yakni material. Material merupakan bahan yang digunakan sebagai penyusun struktur bangunan. Ketersediaan material sebagai komponen penting pada suatu proyek konstruksi memiliki keterkaitan dengan anggaran biaya proyek. Sehingga secara tidak langsung memegang peranan penting dalam menunjang keberhasilan proyek. Namun pada setiap pelaksanaan proyek konstruksi bangunan, kemunculan sisa material tidak akan bisa dihindari. Mengingat bahwa sisa material merupakan masalah yang penting pada industri konstruksi maka perlu dilakukan analisa dan evaluasi sisa material konstruksi pada proyek pembangunan ruko. Dalam penelitian ini, untuk menentukan sisa material yang dominan akan dianalisis berdasarkan prinsip Pareto. Selanjutnya akan dianalisis penyebab sisa material yang dominan menggunakan Fishbone Diagram. Hasil dari penelitian menggunakan metode Pareto, terdapat 1 jenis material yang menimbulkan sisa material dominan, yaitu besi beton dengan total biaya sisa material yang dominan adalah sebesar Rp.31.701.391,18. Berdasarkan analisis menggunakan Fishbone Diagram, faktor-faktor penyebab sisa material pada besi beton disebabkan oleh kesalahan dalam pemotongan sehingga sisa potongan besi terlalu pendek dan tidak bisa digunakan lagi, serta kesalahan dalam perhitungan sehingga terjadi kelebihan material. Kata kunci: sisa material, Metode Pareto, Fishbone Diagram","PeriodicalId":33293,"journal":{"name":"Jurnal Tekno Insentif","volume":"22 88","pages":"761-767"},"PeriodicalIF":0.0,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ejournal.unsrat.ac.id/v3/index.php/tekno/article/download/55343/46258","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147912267","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational Analysis of Horizontally and Inclined Finned Solar Air Collector","authors":"Halefom Kidane, Janos Buzas, Istvan Farkas","doi":"10.36787/jti.v17i2.1172","DOIUrl":"https://doi.org/10.36787/jti.v17i2.1172","url":null,"abstract":"ABSTRACT
 The majority of researchers have carried out simulations using computational fluid dynamics (CFD) and experimental investigation for design, manufacturing, performance evaluation, understanding flow behavior, efficiency prediction, condition optimization, etc. The main objective of the recent paper is to compare horizontally finned and 45° inclined finned solar air collector absorbers and to select the better construction using numerical simulation. ANSYS Fluent Workbench 2022 with renormalization-group (RNG) group k–ε turbulence model was applied for the analysis of the computational domain of solar air heaters. The simulation results generated using CFD were validated with data from the literature, and the simulation results were in agreement with experimental results from the relevant literature. Based on the simulation results the horizontal finned solar air collector absorber has better heat transfer behavior than the 45° inclined.
 ABSTRAK
 Mayoritas peneliti umumnya melakukan simulasi menggunakan perangkat komputasi, seperti computational fluid dynamic (CFD) dan eksperimental untuk menunjang keperluan desain, rancang bangun sistem, evaluasi kinerja dan karakteristik aliran, memperkirakan kinerja, serta optimalisasi, dll. Target dari makalah ini adalah membandingkan pemanas udara surya dengan sirip posisi horizontal dan miring 45° yang dipasang pada kolektor surya untuk memilih konstruksi terbaik secara numerik. ANSYS Fluent Workbench 2022 dengan model turbulensi k–ε renormalisasi grup (RNG) digunakan sebagai perangkat simulasi. Hasil simulasi divalidasi dengan data eksperimental dari literatur yang relevan, dan memperlihatkan kecenderungan yang sama. Berdasarkan simulasi, kolektor udara surya bersirip horizontal mempunyai karakteristik perpindahan panas yang lebih baik dibandingkan dengan kolektor udara bersirip miring 45°.","PeriodicalId":33293,"journal":{"name":"Jurnal Tekno Insentif","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135405823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pemetaan Distribusi Hutan Mangrove Menggunakan Algoritma Machine Learning di Kawasan Hutan Mangrove Petengoran","authors":"Anggun Tridawati, Armijon Armijon, Fajri Yanto, Tika Christy Novianti","doi":"10.36787/jti.v17i2.1101","DOIUrl":"https://doi.org/10.36787/jti.v17i2.1101","url":null,"abstract":"Abstrak
 Hutan mangrove Petengoran adalah ekowisata mangrove di Provinsi Lampung yang terancam punah karna meningkatnya aktivitas pengunjung. Sehingga, informasi persebaran mangrove sangat diperlukan untuk tujuan konservasi. Dewasa ini, banyak peneliti memanfaatkan teknologi pengindraan jauh untuk pemetaan mangrove menggunakan algoritma machine learning. Penelitian ini bertujuan untuk membandingkan algoritma support vector machine (SVM) dan random forest (RF) untuk pemetaan mangrove menggunakan komposit RGB dan NDVI pada citra Sentinel 2A. Hasil penelitian menunjukkan bahwa algoritma RF memberikan akurasi yang lebih tinggi dibandingkan dengan SVM dibuktikan dengan nilai akurasi keseluruhan dan indeks kappa RF sebesar 92,68% dan 0,88, sedangkan pada SVM sebesar 91,86% dan 0,87. Meski demikian, terdapat kesalahan klasifikasi hutan mangrove di kedua algoritma. Hal tersebut disebabkan oleh kemiripan spektral jenis tanaman dan tidak adanya efek topografi. Sehingga, penelitian selanjutnya diharapkan dapat menambahkan efek topografi untuk mendapatkan akurasi yang lebih baik.
 Abstract
 The Petengoran is mangrove ecotourism in Lampung Province which is threatened with extinction due to increased community activity. Thus, information on the distribution of the Petengoran mangroves is needed for conservation. Many researchers use remote sensing technology to map mangroves using machine learning algorithms. This study aims to compare SVM and random forest RF algorithms for mapping mangroves using RGB and NDVI composites on Sentinel 2A imagery. The results showed that the RF algorithm provides higher accuracy compared to SVM. This is evidenced by the overall accuracy value and RF kappa index of 92.68% and 0.88, while the SVM is 91.86% and 0.87. However, there is a misclassification of mangrove forests in both algorithms. This is due to the spectral similarity of vegetation and no topographical effect. Thus, future research is expected to add topographical effects to obtain higher accuracy.","PeriodicalId":33293,"journal":{"name":"Jurnal Tekno Insentif","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135993191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediksi Pola Kedatangan Turis Mancanegara dan Menganalisis Ulasan Tripadvisor dengan LSTM dan LDA","authors":"Fahira Fahira, Cahyo Prianto","doi":"10.36787/jti.v17i2.1096","DOIUrl":"https://doi.org/10.36787/jti.v17i2.1096","url":null,"abstract":"Abstrak
 Penelitian ini menggunakan model LSTM dan LDA untuk meramalkan pola kedatangan wisatawan mancanegara dan menganalisis ulasan Tripadvisor di Jakarta. LSTM memproyeksikan pola kedatangan berdasarkan data historis dan menunjukkan peningkatan jumlah wisatawan dalam periode satu tahun mendatang, sedangkan LDA mengidentifikasi topik utama dalam ulasan dengan tujuan rekomendasi spesifik untuk kota Jakarta. Rekomendasi penelitian meliputi peningkatan pelayanan, kebersihan, infrastruktur, promosi tempat wisata alternatif, dan komunikasi yang jelas kepada wisatawan. Evaluasi menunjukkan performa yang baik, dengan MAPE 2,69% dalam memprediksi kedatangan wisatawan. Penelitian ini menjadi dasar untuk pengambilan keputusan dan perencanaan industri pariwisata Jakarta. Secara keseluruhan, penelitian ini memberikan wawasan berharga untuk pengembangan industri pariwisata Jakarta dengan prediksi akurat dan analisis ulasan wisatawan.
 Abstract
 This study uses the LSTM and LDA models to predict arrival patterns of foreign tourists and analyze Tripadvisor reviews in Jakarta. The LSTM projects arrival patterns based on historical data and shows an increase in the number of tourists in the coming one year period, while the LDA identifies the main topics in the review with specific recommendation objectives for the city of Jakarta. Research recommendations include service improvement, cleanliness, infrastructure, promotion of alternative tourist attractions, and clear communication to tourists. Evaluation shows good performance, with MAPE 2.69% in predicting tourist arrivals. This research forms the basis for decision making and planning for the Jakarta tourism industry. Overall, this research provides valuable insights for the development of Jakarta's tourism industry with accurate predictions and analysis of tourist reviews.","PeriodicalId":33293,"journal":{"name":"Jurnal Tekno Insentif","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135996088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediksi Cuaca Kota Jakarta Menggunakan Metode Random Forest","authors":"Zian Asti Dwiyanti, Cahyo Prianto","doi":"10.36787/jti.v17i2.1136","DOIUrl":"https://doi.org/10.36787/jti.v17i2.1136","url":null,"abstract":"Abstrak
 Prediksi cuaca berperan penting dalam berbagai bidang kehidupan, seperti pertanian, transportasi, pariwisata, dan mitigasi bencana. Kemampuan memprediksi cuaca secara akurat dan tepat waktu sangat berdampak dalam pengambilan keputusan yang cerdas. Kota Jakarta, sebagai ibu kota Indonesia yang padat penduduk dan memiliki aktivitas ekonomi tinggi, membutuhkan sistem prediksi cuaca yang handal untuk mendukung pengelolaan sektor-sektor tersebut. Studi ini bertujuan memprediksi cuaca di Kota Jakarta dengan menggunakan metode Random Forest dan data cuaca historis yang terpercaya dari website OpenData Jakarta. Evaluasi menunjukkan bahwa model Random Forest memberikan prediksi cuaca yang baik, dengan akurasi, presisi dan recall sebesar 0.71, F1-score sebesar 0.70, serta ROC-AUC sebesar 0.92. Metrik evaluasi ini menggambarkan kinerja model dalam mengklasifikasikan cuaca dengan baik, mempertimbangkan keakuratan, ketepatan, dan keseimbangan antara presisi dan recall. Hasil prediksi cuaca tersebut mencakup kemampuan model untuk mengidentifikasi dengan benar berbagai kelas cuaca, dan memberikan informasi berharga dalam pengambilan keputusan terkait kondisi cuaca di Kota Jakarta.
 Abstract
 Weather prediction plays a crucial role across various life domains, including agriculture, transportation, tourism, and disaster mitigation. The ability to predict weather accurately and in a timely manner significantly impacts informed decision-making. Jakarta, as Indonesia's populous capital with high economic activity, necessitates a reliable weather forecasting system to support sector management. This study aims to predict Jakarta's weather using the Random Forest method and dependable historical weather data from the OpenData Jakarta website. Evaluation reveals that the Random Forest model offers favorable weather predictions, boasting an accuracy, precision, and recall of 0.71, an F1-score of 0.70, and an ROC-AUC of 0.92. These evaluation metrics epitomize the model's adeptness in effectively classifying weather, striking a balance between precision and recall. The weather prediction outcomes encompass the model's capacity to accurately identify diverse weather categories, thereby furnishing valuable insights for decision-making concerning Jakarta's weather conditions.","PeriodicalId":33293,"journal":{"name":"Jurnal Tekno Insentif","volume":"80 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135994897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analisis Perbaikan Defect Run Out pada Product Ban Sepeda dengan QCC Method di PT. H.AI","authors":"Iwan Kurniawan, Ernawati Ernawati, Nurwahidah Al Salamah","doi":"10.36787/jti.v17i2.1140","DOIUrl":"https://doi.org/10.36787/jti.v17i2.1140","url":null,"abstract":"ABSTRAK
 H-AI merupakan perusahaan yang memproduksi Ban Dalam dan Ban Luar sepeda, beragam merk. Di tengah permintaan produk yang terus meningkat, masalah bermunculan diantaranya defect, reject, rework dan scrap. Sehingga Perusahaan melakukan program perbaikan kualitas dengan fokus pada pelaksanaan QCC. Salah satu perbaikan yang dilakukan adalah menurunkan Defect Run Out. Data yang digunakan diambil berdasarkan jumlah defect terbanyak dari jumlah produksi pada size tertentu, untuk mengantisipasi jika setiap size memiliki penyebab yang berbeda. Dari hasil perhitungan diperoleh jumlah defect selama tiga bulan sebelum pelaksanaan QCC untuk 5 sizes terbesar adalah 25.497pcs dengan jenis defect terbanyak adalah Run Out sebesar 14.275pcs, atau 56% dari total defect. Setelah dilakukan proses perbaikan, terjadi penurunan sebesar 4,221 pieces selama tiga bulan berikutnya.
 ABSTRACT
 H-AI is a company that manufactures bicycle tires and tires, various brands. Amidst the ever-increasing demand for products, problems have emerged including defects, rejects, rework and scrap. So that the Company carries out a quality improvement program with a focus on QCC implementation. One of the improvements made is to reduce the Defect Run Out. The data used is taken based on the highest number of defects from the number of production at a certain size, to anticipate if each size has a different cause. From the calculation results, it was obtained that the number of defects for three months before the implementation of QCC for the 5 largest sizes was 25,497pcs with the most type of defect being Run Out of 14,275pcs, or 56% of the total defects. After the repair process was carried out, there was a decrease of 4,221 pieces over the following three months.","PeriodicalId":33293,"journal":{"name":"Jurnal Tekno Insentif","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135993038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Klasifikasi Penyakit pada Daun Anggur menggunakan Metode Convolutional Neural Network","authors":"Ken Ratri Wardani, Laurentius Leonardi","doi":"10.36787/jti.v17i2.1130","DOIUrl":"https://doi.org/10.36787/jti.v17i2.1130","url":null,"abstract":"Abstrak
 Penyakit pada daun dapat mengurangi kualitas dan produktivitas. Deteksi atau klasifikasi penyakit pada tanaman memiliki peran yang sangat menonjol di bidang pertanian modern. Penelitian ini bertujuan untuk mengetahui akurasi klasifikasi objek penyakit pada daun dengan metode Convolutional Neural Network dengan menguji ukuran citra, arsitektur, nilai learning rate, dan jumlah epoh untuk mendapatkan akurasi yang baik. Selain itu menerapkan regularisasi Dropout untuk menghindari overfitting dan algoritma optimisasi RMSProp agar laju pembelajaran beradaptasi dengan kondisi data. Dataset yang digunakan berupa citra daun anggur sehat dan berpenyakit, berasal dari PlantVillage. Berdasarkan hasil pengujian, ukuran citra 128 × 128 piksel, arsitektur dengan 5 lapisan konvolusi, nilai learning rate 0.001 dan epoh sebanyak 30 menghasilkan nilai akurasi yaitu 98,5% untuk kelas Black Measles, 98,1% untuk kelas Black Rot dan 99,5% untuk kelas Healthy.
 Abstract
 Leaf diseases can reduce quality and productivity. Detection or classification of diseases in plants has a very prominent role in modern agriculture. This study aims to determine the classification accuracy of disease objects on leaves using the Convolutional Neural Network method by testing image size, architecture, learning rate, and number of epohs to obtain good accuracy. Besides that, it applies Dropout regularization to avoid overfitting and the RMSProp optimization algorithm so that the learning rate adapts to data conditions. The dataset used is in the form of images of healthy and diseased grape leaves, from PlantVillage. Based on the test results, the image size is 128 × 128 pixels, the architecture with 5 convolution layers, the learning rate is 0.001 and the epoh is 30, which results in an accuracy value of 98.5% for the Black Measles class, 98.1% for the Black Rot class and 99.5 % for Healthy class..","PeriodicalId":33293,"journal":{"name":"Jurnal Tekno Insentif","volume":"272 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135993039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pendekatan Supervised Learning untuk Diagnosa Kehamilan","authors":"Fahira Fahira, Zian Asti Dwiyanti, Roni Habibi","doi":"10.36787/jti.v17i2.1102","DOIUrl":"https://doi.org/10.36787/jti.v17i2.1102","url":null,"abstract":"Abstrak
 Dalam studi ini dilakukan evaluasi performa dari beberapa algoritma machine learning dalam mendiagnosis kehamilan. Tujuan dari studi ini adalah untuk menemukan algoritma yang paling efektif antara decision tree dengan random forest dalam mendiagnosis kehamilan. Studi ini juga bertujuan untuk memberikan pengetahuan yang lebih baik mengenai bagaimana teknik pembelajaran mesin dapat digunakan dalam proses diagnosa kehamilan dan memberikan informasi yang berguna bagi para dokter dan peneliti dalam membuat keputusan yang tepat dalam mendiagnosis kehamilan. Dari hasil evaluasi dapat disimpulkan bahwa model Random Forest dengan menggunakan dataset yang seimbang dan metode Gini memiliki akurasi terbaik sebesar 81%. Hal ini menunjukkan bahwa menggunakan dataset yang seimbang dapat meningkatkan performa dalam mendiagnosis kehamilan. Algoritma Random Forest merupakan metode yang sering digunakan dalam proses pengklasifikasian karena kinerjanya yang baik. Algoritma ini bekerja dengan membuat pohon keputusan yang digunakan untuk membuat prediksi. Pada studi ini, kami menggunakan algoritma Random Forest dengan metode Gini untuk melakukan prediksi kehamilan.
 Abstract
 This study evaluates the performance of several machine learning algorithms in diagnosing pregnancy. The purpose of this study is to find the most effective algorithm between decision trees and random forests in diagnosing pregnancy. This study also aims to provide better knowledge about how machine learning techniques can be used in the process of diagnosing pregnancy and provide useful information for doctors and researchers in making the right decisions in diagnosing pregnancy. From the evaluation results it can be concluded that the Random Forest model using a balanced dataset and the Gini method has the best accuracy of 81%. This shows that using a balanced dataset can improve performance in diagnosing pregnancy. Random Forest Algorithm is a method that is often used in the classification process because of its good performance. This algorithm works by creating a decision tree that is used to make predictions. In this study, we used the Random Forest algorithm with the Gini method to predict pregnancy.","PeriodicalId":33293,"journal":{"name":"Jurnal Tekno Insentif","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135995046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FAKTOR-FAKTOR YANG MEMPENGARUHI KEPUTUSAN PEMBELIAN PRODUK","authors":"Yanti Pasmawati, M Pegi Andrian","doi":"10.33557/jtekno.v20i1.2559","DOIUrl":"https://doi.org/10.33557/jtekno.v20i1.2559","url":null,"abstract":"In this modern era, competition in the business world is getting tougher. Every company is competing to market their products or services according to the growing trend among consumers. Likewise in the furniture business which is also starting to develop quite rapidly in Indonesia. The company basically wants to be the market leader in the competition faced in the business world. PT Pratama Tunggu Tubang is one of the PTs working in the timber industry. Based on the results of this study, simultaneously the product design variable (X1) and product quality variable (X2) in this study have a positive and significant effect on purchasing decisions (Y) on PT consumers Primary Wait Tubang. This is evidenced by the results of calculations from SPSS obtained f count 291.751> f table 3.150, so it can be concluded that the product design variable (X1) and product quality variable (X2) have a simultaneous effect on the purchase decision variable (Y).
 Keywords: design product,, product, quality product, purchase decision","PeriodicalId":33293,"journal":{"name":"Jurnal Tekno Insentif","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135466291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SIMULASI WATER SEPARATOR PADA GATHERING STATION MENGGUNAKAN ZELIO SMART RELAY","authors":"Aminudin, N. Fitri","doi":"10.33557/jtekno.v20i1.2309","DOIUrl":"https://doi.org/10.33557/jtekno.v20i1.2309","url":null,"abstract":"Alat simulasi bekerja untuk memisahkan air dengan minyak pada tangki pengumpul dengan menggunakan smart relay.  Pada alat menggunakan 4 buah tangki yaitu 2 buah tangki sumber yang berisi minyak dan air. Kedua tangki tersebut akan diaktifkan secara bersamaan kedalam tangki pencampur. Setelah terjadi campuran dan sensor elektroda mendeteksi volume air maksimum sehingga akan mengaktifkan pompa 1 untuk memindahkan air Kembali ke tangki air. Pompa 1 akan berhenti bekerja pada saat sensor elektroda menyentuh minyak di level tertentu. Dengan melakukan pengaturan waktu pada jam tertentu,  pompa 2 akan bekerja memindahkan minyak ke tangki lainnya sesuai dengan waktu yang di tentukan. Alat ini sudah bekerja dengan cukup baik dalam pengaturan on off pompa,  ","PeriodicalId":33293,"journal":{"name":"Jurnal Tekno Insentif","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77000236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}