{"title":"Audit Sistem Informasi Persediaan Aksesoris Handphone Pada PT. Redangus Swakarya Satu Menggunakan Framework Cobit 4.0","authors":"Mulia Rahmayu, Renjiro Joshua Mantovani","doi":"10.31294/reputasi.v4i1.1966","DOIUrl":"https://doi.org/10.31294/reputasi.v4i1.1966","url":null,"abstract":"Audit sistem informasi persediaan barang digunakan untuk mengukur sejauh mana sistem telah terlaksana dengan baik yang ada pada PT. Redangus Swakarya Satu dan akan memberikan masukan rekomendasi perbaikan, suatu sistem persediaan barang yang baik seharusnya menjamin bahwa segala sesuatunya berjalan seperti yang seharusnya, maka secara periodik diperlukan adanya pemeriksaan audit system, agar tidak terjadi lagi pada saat penginputan data persediaan barang yang ada salah masukan, selama ini masih ada kesalahan penginputan barang dengan nama yang sama tetapi ukuran yang berbeda dan sistem persediaan barang merupakan teknologi yang biasanya digunakan oleh PT. Redangus Swakarya Satu, penjualan barang untuk mendukung bagian gudang dalam melakukan pendataan mengenai persediaan barang, metode yang di gunakan deskriptif analisis dengan pendekatan kuantitatif yaitu penelitian yang kemudian diolah dan dianalisis untuk diambil kesimpulan. Hasil evaluasi Sistem Informasi persediaan barang pada sub-domain PO7 yaitu menentukan rencana strategis TI di peroleh 4 – Managed and measureabel (4,156) dengan nilai gap (0,156), PO.8 Ensure compliance with external requirements, organisasi dan hubunganya diperoleh 4 – Managed and measureabel (4,048) dengan nilai gap (0,048), AI.1 Identify automated solutions diperoleh 4 - Managed and Measurable (4,083) dengan nilai gap (0,083), AI.2 Acquire and maintain Application Software diperoleh 4 - Managed and Measurable (4,209) dengan nilai gap (0,209, ME.1 Monitor and Evaluate IT Performance diperoleh 4 - Managed and Measurable (4,111) dengan nilai gap (0,111), DS.2 Supplier Relationship Management diperoleh 4 - Managed and Measurable (4,196)dengan nilai gap (0,196).","PeriodicalId":33961,"journal":{"name":"Jurnal Informatika dan Rekayasa Perangkat Lunak","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85348879","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}
Mohamad Tafrikan, Ariska Kurnia Rachmawati, Atika Dewi Ardiyanti, R. Saputri, Sholifatun Umayah
{"title":"Penentuan E-Wallet Terbaik dengan Metode Simple Additive Weighting (SAW) dan Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)","authors":"Mohamad Tafrikan, Ariska Kurnia Rachmawati, Atika Dewi Ardiyanti, R. Saputri, Sholifatun Umayah","doi":"10.36499/jinrpl.v5i1.7718","DOIUrl":"https://doi.org/10.36499/jinrpl.v5i1.7718","url":null,"abstract":"Currently, many purchases of goods can be found electronically or known as e-wallets. E-wallet is a form of Fintech (Finance Technology) that utilizes internet media and is used as an alternative payment method. Several other types of e-wallets: Dana, Shopeepay, Gopay, Ovo, Sakuku. This study discusses the best recommendations among types of E-wallet using the Simple Additive Weighting (SAW) method and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Both of these methods are able to make more accurate assessments and predetermined preference weights. Based on calculations using the SAW and TOPSIS methods, the first best e-wallet ranking is DANA, the second is Shopeepay, and the third is Gopay.","PeriodicalId":33961,"journal":{"name":"Jurnal Informatika dan Rekayasa Perangkat Lunak","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49209259","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}
Aditiya Hermawan, A. Halim, Dera Susilawati, Intan Anjali Putri
{"title":"Implementasi Algoritma Advance Encryption Standard dan Caesar Cipher pada Pesan Terenkripsi","authors":"Aditiya Hermawan, A. Halim, Dera Susilawati, Intan Anjali Putri","doi":"10.36499/jinrpl.v5i1.6714","DOIUrl":"https://doi.org/10.36499/jinrpl.v5i1.6714","url":null,"abstract":"The development of information technology allows many people to communicate at any time with various media, one of which is the exchange of messages. However, many people do not realize that there are security holes that are used by irresponsible parties to commit crimes such as theft of messages, intercepting messages, and changing the contents of messages. One technique for securing messages is cryptography. Cryptography is the science and art of keeping messages secure when messages are sent from one place to another. Several cryptographic methods that can be used are Advanced Encryption Standard and Caesar Cipher, because the Advanced Encryption Standard method has high security for message security while the Caesar Cipher method has the advantage of being fast in calculations. The combination of these two algorithms in making secret messages is implemented in the form of a mobile-based application to make it easier for users to make secret messages. The results of the encrypted messages formed are difficult to decrypt because they go through 2 stages of the encryption process so that the contents of important messages can be guaranteed confidentiality.","PeriodicalId":33961,"journal":{"name":"Jurnal Informatika dan Rekayasa Perangkat Lunak","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43526738","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 IT Governance Measurement using Cobit 5 Framework in Quality Assurance Department","authors":"A. Nurdin, M. Lubis","doi":"10.36499/jinrpl.v5i1.7963","DOIUrl":"https://doi.org/10.36499/jinrpl.v5i1.7963","url":null,"abstract":"This research is conducted to measure and evaluate of IT governance at quality assurance department of XYZ university. The measurement used the COBIT 5 framework by Process Assesment Model. The objective of this research is performing the measurement and evaluation of the IT Governance in the Quality Assurance Division of XYZ University with DSS domains in COBIT 5 Framework. The approach utilized the primer data from interview and questionaire, and secondary data from observation and document stages. The result of this research show the most of the capability level process of IT Governance are at Level 1 (performed) they are 4 processes form DSS domain (DSS01, DSS04, DSS05, DSS06) and at Level 2 (DSS02, DSS03). The conclusion of this research show most of the process perform capability level 1 today.","PeriodicalId":33961,"journal":{"name":"Jurnal Informatika dan Rekayasa Perangkat Lunak","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47993481","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":"Implementasi Business Intelligence untuk Analisa dan Visualisasi Data Penyebab Kematian Di Indonesia Menggunakan Platform Tableau","authors":"Lathifah Dini Rachmawati, Firmanul Hasan","doi":"10.36499/jinrpl.v5i1.7584","DOIUrl":"https://doi.org/10.36499/jinrpl.v5i1.7584","url":null,"abstract":"Factors causing death in Indonesia are increasing, and many of them cannot identified with certainty. This study aims to identify the most common causes of death in Indonesia. such as causes of death due to social disasters, causes of death due to natural disasters, and causes of death due to non-natural disasters and diseases. This study employs a dataset method from www.kaggle.com with a time span of 2012-2021. The results of this study are reports in the form of a visualization dashboard using Tableau that contains information on the most common causes that occur in Indonesia with a range from 2012 to 2021 so that it can be used as a reference to support decision-making. In conclusion, the number of deaths in 2012–2021 was caused by the highest number of deaths due to natural disasters caused by earthquakes and tsunamis, which accounted for 2,615 deaths. Furthermore, the most cases of death due to non-natural disasters and diseases were caused by COVID-19, namely 144,094 deaths. And the most cases of death due to social disasters were caused by social conflict or social unrest, with 69 deaths.","PeriodicalId":33961,"journal":{"name":"Jurnal Informatika dan Rekayasa Perangkat Lunak","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45841566","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":"Rancangan Sistem Klasifikasi Kekurangan Gizi Balita Dengan Metode K-Nearest Neighbor","authors":"Syahrani Lonang, A. Yudhana, M. K. Biddinika","doi":"10.36499/jinrpl.v5i1.7834","DOIUrl":"https://doi.org/10.36499/jinrpl.v5i1.7834","url":null,"abstract":" Malnutrition in toddlers is a serious problem faced by developing countries like Indonesia, and the resulting long-term effects can reduce the intelligence of toddlers. The classification of the nutritional status of children under five is still carried out conventionally in community health centers. The K-Nearest Neighbor algorithm is included in a machine learning algorithm that can be used to classify one of the nutritional status classification problems. K-NN is used as a class determination algorithm for new data to be input according to the format. This research begins with a literature study, then identifies needs, followed by data collection that is planned to be used in the system to be built as well as a reference for making the design and the final stage of system design. This research succeeded in creating a system design using the Unified Model Language (UML), one use case that contains four functional systems, including uploading dataset files, displaying datasets, testing the accuracy of datasets, predicting new data, and designing system interfaces that will make system development easier..","PeriodicalId":33961,"journal":{"name":"Jurnal Informatika dan Rekayasa Perangkat Lunak","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46413121","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":"Klasterisasi Pendidikan Masyarakat Untuk Mengetahui Daerah Dengan Pendidikan Terendah Menggunakan Algoritma K-Means","authors":"Nurahman Nurahman, Dian Aulia","doi":"10.36499/jinrpl.v5i1.7510","DOIUrl":"https://doi.org/10.36499/jinrpl.v5i1.7510","url":null,"abstract":"Education is a basic need for every human being who plays an important role in the future of the nation, because a nation that is said to be advanced can be seen from its good learning system. Successful education is measured by the average number of graduates at various levels of education in various regions. But not all regions are good in the quality of education. One of them is the area in Indonesia, such as the Kapuas district, Central Kalimantan. It is known that in previous years this area lacked improvement in education, causing several areas where people did not go to school or dropped out of school. Many of the problems are caused by economic factors, laziness, lack of motivation about the importance of education, and so on. The previous Covid-19 pandemic was also the reason for the increase in the number of children dropping out of school due to a declining family economy. The number of areas in Kapuas district requires grouping the number of existing villages. The grouping aims to make it easier for the government to pay special attention to areas where education is considered lacking and other purposes are to find out which villages have low levels of education. In grouping, the system applied is data mining using the K-Means Algorithm Clustering method which is processed using rapidminer software. The groupings formed on the education level data of 229 records are 8 clusters where the lowest education villages are stated in (C1) with a total of 33 villages.","PeriodicalId":33961,"journal":{"name":"Jurnal Informatika dan Rekayasa Perangkat Lunak","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43159373","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":"Penerapan Algoritma Backpropagation Untuk Text Recognition Yang Ditranslate Ke Bahasa Daerah","authors":"Somantri, Pascal Aditia Muclis, Ivan Kharisma","doi":"10.36499/jinrpl.v5i1.6998","DOIUrl":"https://doi.org/10.36499/jinrpl.v5i1.6998","url":null,"abstract":"Every area of the community there is information, news, announcements or notifications through print media such as pictures, banners, posters and paper. However, there are many foreign languages (English) in the information which makes people not understand the language, because people only understand their own regional language (Sundanese) and the lack of media to help people understand the regional language (Sundanese) to a foreign language (English). Therefore, the author takes research on android-based text detection. Text detection or called Optical Character Recognition (OCR) is a system that can detect text into data files that can be processed in such a way. The method of collecting data or information in this study uses qualitative, the method for developing this system uses the waterfall method which has the advantage of being able to accelerate system development and using a backpropagation algorithm for text recognition. Backpropagation algorithm is an algorithm that can minimize errors, so as to make text detection more accurate. The results of the text detection accuracy are 97% and the error accuracy results are 3%, the text accuracy results are taken from a total of 14 samples, and 68 words. The results of text detection are affected by several cases such as the type of text font, the level of light in the image and the direction in which the photo or image is taken.","PeriodicalId":33961,"journal":{"name":"Jurnal Informatika dan Rekayasa Perangkat Lunak","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43719867","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":"Panduan Pengukuran Perangkat Lunak Metode Function Point Serta Implementasinya pada Website Pemesanan Tiket Bus","authors":"Rizky Parlika, Ahmad Maghfur’ Ali, Ardiana Deka Maharani, Bregsi Atingsari Julastri, Syalum Marsya Pruista","doi":"10.36499/jinrpl.v5i1.6633","DOIUrl":"https://doi.org/10.36499/jinrpl.v5i1.6633","url":null,"abstract":"Measuring software quality is an important thing for software engineering. The purpose of applying measurements to software processes is to find out how complex and useful the software is. The measurement method used is Allan Albrecht's method, namely the Function Point Analysis method, which was developed by the International Function Point User Group (IFPUG). In this study, the authors measured the quality of a bus ticket booking website. Prior to the measurement stage, the authors compiled a module that contains the limitations of each point so that it can be used as a guide for scoring at each measurement stage, namely the stage of calculating the Crude Function Point (CFP) and Relative Complexity Adjustment Factor (RCAF). A calculator application is also made that can automatically calculate the total of function points so that it can make measurements easier. After the total Function Point value of the website is found, the total value can be used to predict the price or cost of making the software.","PeriodicalId":33961,"journal":{"name":"Jurnal Informatika dan Rekayasa Perangkat Lunak","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48758412","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":"Pengembangan Sistem Pendeteksi Jenis Sayuran dengan Metode CNN Berbasis Android","authors":"Rere Setiyo Budiawan, B. Hartono","doi":"10.36499/jinrpl.v5i1.7833","DOIUrl":"https://doi.org/10.36499/jinrpl.v5i1.7833","url":null,"abstract":"Vegetables are foodstuffs of plant origin that can be consumed fresh and have various health benefits. However, not a few people do not know the types of vegetables and will find it difficult to find the vegetables they want. This research aims to make it easier for people to find vegetables by classifying them. Researchers developed a model using the Convolutional Neural Network (CNN) method with a total of 15 datasets with a total of 3000 image data. Researchers conducted training datasets with 3 types of epochs, including 20 epochs, 50 epochs and 100 epochs. The training produces accuracy and training loss, with the highest accuracy belonging to Epoch 50 and Epoch 100 and the lowest level of training loss is owned by Epoch 100 with a total of 0.609. However, after the model was deployed, the accuracy results obtained were not as high as the tests conducted on Google Colab. Tests were carried out on several objects, including carrots with an accuracy of 69%, cabbage with an accuracy of 53%, and papaya with an accuracy of 82%. The difference in accuracy results may be caused by objects that are less identical to the datasets or can also be caused by imperfect models. Even so, this application can already be used to classify types of vegetables.","PeriodicalId":33961,"journal":{"name":"Jurnal Informatika dan Rekayasa Perangkat Lunak","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49460602","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}