Sistemasi Jurnal Sistem Informasi最新文献

{"title":"Recommendations of Thesis Supervisor using the Cosine Similarity Method","authors":"H. Hairani, Mujahid Mujahid","doi":"10.32520/stmsi.v11i3.2003","DOIUrl":"https://doi.org/10.32520/stmsi.v11i3.2003","url":null,"abstract":"Selama proses penulisan skripsi, peran dosen pembimbing sangat dibutuhkan agar penyelesaiannya tepat waktu sehingga kualitas skripsi terjaga. Salah satu permasalahan dalam penentuan dosen pembimbing skripsi S1 ilmu komputer Universitas Bumigora adalah adanya unsur subyektifitas ketua prodi dalam penentuan dosen pembimbing skripsi. Tidak hanya itu, proses pemilihan dosen pembimbing dilakukan secara manual sehingga dapat memakan waktu lama dan dapat memperlambat pengerjaan skripsi mahasiswa. Pengerjaan skripsi mahasiswa menjadi terlambat dan tidak tepat waktu, jika topik skripsinya tidak sesuai dengan kompetensi dosen. Penelitian ini bertujuan implementasi metode cosine similarity untuk rekomendasi dosen pembimbing skripsi S1 ilmu komputer Universitas Bumigora. Tahapan penelitian ini terdiri dari pengumpulan Abstract During the thesis writing process, the role of the supervisor is needed so that the completion of the thesis is timely so that the quality of the thesis is maintained. One of the problems in determining the thesis supervisor for undergraduate computer science at Bumigora University is the subjectivity of the head of the study program in determining the thesis supervisor. Not only that, the selection process for supervisors is done manually so that it can take a long time and can slow down student thesis work. Students' thesis work will be late and not on time if the thesis topic is not in accordance with the competence of the lecturer. This study aims to apply the cosine similarity method to the recommendation of a thesis supervisor for undergraduate computer science at Bumigora University. The stages of this research consist of collecting thesis documents, pre-processing text (Case Folding, Tokenization, Filtering, Stemming), word weighting with TF-IDF, implementation of the cosine similarity method, and accuracy testing. The data used are 113 thesis documents which are divided as training data as many as 90 documents and testing data 23 documents. Based on the testing data on the test, the cosine similarity method can correctly recommend 21 of 23 thesis documents with an accuracy of 91.3%. Thus, the cosine similarity method can be applied to the case of selecting a thesis supervisor for undergraduate computer science at Bumigora University because it has very good accuracy.","PeriodicalId":32367,"journal":{"name":"Sistemasi Jurnal Sistem Informasi","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76210427","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":"Best Alpha for Forecasting Stock using Brown's Weighted Exponential Moving Average","authors":"Muhammad Amfahtori Wijarnoko, M. K. Sophan, K. E. Permana","doi":"10.32520/stmsi.v11i3.1588","DOIUrl":"https://doi.org/10.32520/stmsi.v11i3.1588","url":null,"abstract":"Stocks are a very profitable type of investment, one of the most profitable stock sectors is mining. PT. Aneka Tambang Tbk (ANTM) is the largest mining company in Indonesia engaged in metals and minerals. The problem that occurs in stock investment is the fluctuation of stock prices. Stock price fluctuations occur because of the process of buying and selling shares, rumors about the company, or certain policies made by the government that can affect the company. These rapid stock price fluctuations are very dangerous for investors because these fluctuations are not always directly proportional to the company's fundamental conditions. Therefore we need a system that can perform forecasting to monitor stock price fluctuations that occur. The author uses the Brown's Weighted Exponential Moving Average (B-WEMA) method for forecasting. The data used are 241 daily PT Aneka Tambang Tbk (ANTM) in the Covid-19 era, starting January 1, 2020 - December 31, 2020. The experiment was carried out into 3 scenarios, namely using 3 months, 6 months, and 12 months data. Alpha tested is 0.1 to 0.9 (1 digit behind the comma) while the number of moving averages is 3, 5, 10, 20, 50, 100, and 200 adjusted to the amount of data. From the experiment it was found that the best forecasting was obtained in scenario 2 with the best manual alpha value having a MAPE error value of 2.84% using an alpha of 0.9 and a moving average of 100.","PeriodicalId":32367,"journal":{"name":"Sistemasi Jurnal Sistem Informasi","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73264363","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":"Identification of Barangan Banana Ripeness Treatment Types using k-Nearest Neighbor","authors":"A. Abdullah, Rendi Azrian","doi":"10.32520/stmsi.v11i3.2281","DOIUrl":"https://doi.org/10.32520/stmsi.v11i3.2281","url":null,"abstract":"","PeriodicalId":32367,"journal":{"name":"Sistemasi Jurnal Sistem Informasi","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82856328","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":"Gold Price Prediction Based on Gold.org Data Using the Long Short Term Memory Method","authors":"Vincentius Riandaru Prasetyo, Stefan Axel, Juan Timothy Soebroto, David Sugiarto, Septian Ardi Winatan, Senradel Daniel Njudang","doi":"10.32520/stmsi.v11i3.1999","DOIUrl":"https://doi.org/10.32520/stmsi.v11i3.1999","url":null,"abstract":"Metode LSTM atau Long Short Term Memory dapat diimplementasikan untuk melakukan prediksi harga emas berdasarkan harga emas sebelumnya. Model prediksi yang dibangun pada penelitian ini memprediksi harga emas kedepan berdasarkan 60 data harga emas sebelumnya. Berdasarkan hasil pengukuran akurasi yang dilakukan, didapatkan akurasi sebesar 87,84% dengan nilai rentang selisih antara harga asli dengan prediksi sebesar 5 dan jumlah epoch yaitu 100. Abstract Gold is one form of precious metal with priceless value in this day and age. Therefore, many people are starting to invest in gold. Someone who wants to invest in gold must pay attention to changes in gold's buying and selling price. One site that can be used as a reference to see changes in the price of buying and selling gold is gold.org. Several factors influence gold price changes, namely changes in the value of the US Dollar exchange rate, the amount of world gold production, and the increase in demand for gold itself. This means that the price of gold tends to be unstable because of frequent changes. The LSTM or Long Short Term Memory method can be implemented to predict gold prices based on previous gold prices. The prediction model built in this study predicts gold price in the future based on 60 last gold price data. Based on the results of accuracy measure, an accuracy of 87.84% was obtained with the value of the difference between the original price and the prediction of 5 and the number of epochs being 100.","PeriodicalId":32367,"journal":{"name":"Sistemasi Jurnal Sistem Informasi","volume":"49 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89481559","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":"The Analysis of Online Worship Services Acceptance using the UTAUT 2 Method and Clustering k-Means","authors":"Gilang Jonathan Phita, Yessica Nataliani","doi":"10.32520/stmsi.v11i3.2020","DOIUrl":"https://doi.org/10.32520/stmsi.v11i3.2020","url":null,"abstract":"penerimaan teknologi pelayanan ibadah satu tolak mengetahui menerima sebuah teknologi. Tujuan dari penelitian ini yaitu untuk melihat penerimaan teknologi jemaat terhadap pelayanan ibadah online menggunakan Unified Theory of Acceptance and Use of The Technology 2 (UTAUT 2), dan jawaban responden dari berbagai kalangan dan usia dikelompokkan menggunakan clustering k-Means . Pengumpulan data dilakukan melalui kuesioner dengan jumlah responden sebanyak 220 responden. Dari uji regresi linear berganda terhadap variabel UTAUT 2 didapatkan bahwa variabel Habit pada penggunaan ibadah online memiliki pengaruh yang tinggi terhadap variabel Behavioral Intention . Selain itu variabel Habit pada penggunaan ibadah online memiliki pengaruh yang tinggi terhadap variabel Use Behavior . Selanjutnya, clustering dengan k-Means dilakukan untuk melihat kelompok usia yang puas terhadap ibadah online, berdasarkan empat cluster yang didapat menggunakan metode Elbow dan enam kelompok usia. Hasil dari clustering k Means dari seluruh variabel UTAUT 2, kelompok usia 12-16 tahun dan 26-35 tahun merupakan kelompok yang paling puas dan menerima ibadah online . Abstract The characteristics and background of the church congregation and the acceptance of technology in online worship services were one of the parameter to determine user attitudes or behavior in accepting technology. The objective of this research to is to find out the acceptance of church congregation's technology toward the online worship services using the Unified Theory of Acceptance and Use of The Technology 2 (UTAUT 2), and answers of respondents from various backgrounds and ages were grouped using k-Means clustering. Data were collected through a questionnaire with a total of 220 respondents. From the UTAUT 2 method using multiple linear regression, it was found that the Habit variable on the use of online worship has a high influence toward the Behavioral Intention variable, and the Habit variable on the use of online worship has a high influence toward the Use Behavior variable. Furthermore, clustering with k-Means was carried out to see the age group that was satisfied with online worship services, based on four clusters obtained using the Elbow method and six age groups. The results of the k-Means clustering of all UTAUT 2 variables, the age group 12-16 years and 26-35 years are the most satisfied group and accept online worship services.","PeriodicalId":32367,"journal":{"name":"Sistemasi Jurnal Sistem Informasi","volume":"56 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88275184","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":"Nave Bayes Optimization with PSO for Predicting ICU Needs for Covid-19 Patients","authors":"L. Lestari, Iqbal Harifal, Taslim Taslim, Yogi Yunefri, Susi Handayani, Eka Sabna, Kursiah Warti Ningsih","doi":"10.32520/stmsi.v11i3.2094","DOIUrl":"https://doi.org/10.32520/stmsi.v11i3.2094","url":null,"abstract":"","PeriodicalId":32367,"journal":{"name":"Sistemasi Jurnal Sistem Informasi","volume":"25 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78503427","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":"X-Ray Classification of Pneumonia by Neural Networks Convolution using Vgg Architecture","authors":"Toni Arifin, Naufal Hidayah Surya","doi":"10.32520/stmsi.v11i3.1597","DOIUrl":"https://doi.org/10.32520/stmsi.v11i3.1597","url":null,"abstract":"","PeriodicalId":32367,"journal":{"name":"Sistemasi Jurnal Sistem Informasi","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91062354","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":"Process Mining Dwelling Time at the Container Port using the Alpha Miner Method","authors":"Rivalno Firmansyah, Herdy Riyaldy, Bambang Jokonowo","doi":"10.32520/stmsi.v11i3.1501","DOIUrl":"https://doi.org/10.32520/stmsi.v11i3.1501","url":null,"abstract":"The problem of measuring the dwelling time of the container logistics process at ports in developing countries is often a major problem. Therefore, process mining as a field of data science that focuses on analyzing event log data is used to perform business process analysis. In process mining, the process is a sequence of events or activities that are carried out to achieve certain goals. Event logs help an organization to find gaps between the designed business processes and the reality of the processes that occur. In this study PM4PY as a python library is used to perform process mining techniques. The results of the fitness calculation in this study indicate that the container logistics business process model is close to 0.99.","PeriodicalId":32367,"journal":{"name":"Sistemasi Jurnal Sistem Informasi","volume":"2088 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91335369","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":"Evaluation of Usability Quality Between InDriver and Maxim Applications Using Usability Scale (SUS) and Usability Testing Methods","authors":"Janet Livia Tuwanakotta, Andeka Rocky Tanaamah","doi":"10.32520/stmsi.v11i3.2001","DOIUrl":"https://doi.org/10.32520/stmsi.v11i3.2001","url":null,"abstract":"Indriver application and Maxim application are international companies engaged in Information Technology where the company provides a platform that brings drivers and customers together quickly. Both applications come from Russia. These two applications are growing rapidly so they have been downloaded more than 5 million times through the Google Play store. Although it has been downloaded more than 5 million times, some users complain about the two applications. From some of the user feedback, the InDriver application and Maxim application still have problems that occur in their users. This problem is part of the usability problems that exist in both applications. So it is necessary to dig into a more comprehensive problem to explore the problems that occur in both applications. You also need to know the level of usability of these two applications. The level of usability needs to be known to explain the usability of these two applications is already at which level from the user side. This study provides an evaluation to determine the usability problems and the level of usability that exist in both applications using the System Usability Scale (SUS) and Usability Testing methods. The results of this study indicate that the features of the Maxim application are still http://sistemasi.ftik.unisi.ac.id not accepted by users seen from the sus questionnaire score of 48.3 and on the rating scale including the F rating with a smaller score of 51 so that it can be concluded that the Maxim application is not included in the acceptable category but not acceptable and the InDriver application has a SUS score of 51.25. On the rating scale, the InDriver application is included in the F rating so it can be concluded that the InDriver application is not included in the acceptable category but the low category.","PeriodicalId":32367,"journal":{"name":"Sistemasi Jurnal Sistem Informasi","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74096637","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":"Classification using the C4.5 Algorithm in Predicting Students Organizational Status Amikom University Yogyakarta","authors":"Neni Firda Wardani Tan, Kusnawi Kusnawi","doi":"10.32520/stmsi.v11i3.2054","DOIUrl":"https://doi.org/10.32520/stmsi.v11i3.2054","url":null,"abstract":"penelitian ini setelah dilakukannya pra-pemrosesan data. Dari hasil pengujian didapatkan rule atau pohon keputusan yang dapat dijadikan aturan dalam menentukan mahasiswa aktif organisasi. Nilai accuracy pada pengujian model ini dengan menggunakan cross validation menghasilkan nilai sebesar 59.27% atau dengan perbandingan data training : data testing sebesar 4 : 1 atau 80% : 20%. Abstract Participation in an organization as a college student is an important way to self-improvement. This research analyzes students' learning in an organization and whether the student is active or not active in an organization using the Decision Tree C4.5 algorithm. Attributes analyzed were out-of-campus organization, previous organization experience, public speaking, problem-solving, confidence level, and personality. From a question from propagating on October 2021, the researcher collect 203 raw data, with 167 processes ones that were used in this research. The test conducted results in a tree or the decision tree that could be used to decide how active a student is in an organization. The accuracy value of this test using cross-validation resulted in a score of 59.27% or in comparison to the data training: data testing of 4:1 or 80%: 20%.","PeriodicalId":32367,"journal":{"name":"Sistemasi Jurnal Sistem Informasi","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80080300","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}