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Jurnal Teknologi dan Sistem Komputer
Published by Universitas Diponegoro
ISSN : 26204002     EISSN : 23380403     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
Jurnal Teknologi dan Sistem Komputer (JTSiskom, e-ISSN: 2338-0403) adalah terbitan berkala online nasional yang diterbitkan oleh Departemen Teknik Sistem Komputer, Universitas Diponegoro, Indonesia. JTSiskom menyediakan media untuk mendiseminasikan hasil-hasil penelitian, pengembangan dan penerapannya di bidang teknologi dan sistem komputer, meliputi sistem embedded, robotika, rekayasa perangkat lunak dan jaringan komputer. Lihat fokus dan ruang lingkup JTSiskom. JTSiskom terbit 4 (empat) nomor dalam satu tahun, yaitu bulan Januari, April, Juli dan Oktober (lihat Tanggal Penting). Artikel yang dikirimkan ke jurnal ini akan ditelaah setidaknya oleh 2 (dua) orang reviewer. Pengecekan plagiasi artikel dilakukan dengan Google Scholar dan Turnitin. Artikel yang telah dinyatakan diterima akan diterbitkan dalam nomor In-Press sebelum nomor regular terbit. JTSiskom telah terindeks DOAJ, BASE, Google Scholar dan OneSearch.id Perpusnas. Lihat daftar pengindeks. Artikel yang dikirimkan harus sesuai dengan Petunjuk Penulisan JTSiskom. JTSiskom menganjurkan Penulis menggunakan aplikasi manajemen referensi, seperti Mendeley, Endnote atau lainnya. Penulis harus register ke jurnal atau jika telah teregister, dapat langsung log in dan melakukan lima langkah submisi artikel. Penulis harus mengupload Pernyataan Pengalihan Hak Cipta saat submisi. Artikel yang terbit di JTSiskom akan diberikan nomer identifier unik (DOI/Digital Object Identifier) dan tersedia serta bebas diunduh dari portal JTSiskom ini. Penulis tidak dipungut biaya baik untuk pengiriman artikel maupun pemrosesan artikel (lihat APC/Article Processing Charge). Jurnal ini mengimplementasikan sistem LOCKSS untuk pengarsipan secara terdistribusi di jaringan LOCKSS privat.
Arjuna Subject : -
Articles 8 Documents
 1 
Search results for , issue "Volume 10, Issue 1, Year 2022 (January 2022)" : 8 Documents clear
Data scaling performance on various machine learning algorithms to identify abalone sex Willdan Aprizal Arifin; Ishak Ariawan; Ayang Armelita Rosalia; Lukman Lukman; Nabila Tufailah
Jurnal Teknologi dan Sistem Komputer Volume 10, Issue 1, Year 2022 (January 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2021.14105

Abstract

This study aims to analyze the performance of machine learning algorithms with the data scaling process to show the method's effectiveness. It uses min-max (normalization) and zero-mean (standardization) data scaling techniques in the abalone dataset. The stages carried out in this study included data normalization on the data of abalone physical measurement features. The model evaluation was carried out using k-fold cross-validation with the number of k-fold 10. Abalone datasets were normalized in machine learning algorithms: Random Forest, Naïve Bayesian, Decision Tree, and SVM (RBF kernels and linear kernels). The eight features of the abalone dataset show that machine learning algorithms did not too influence data scaling. There is an increase in the performance of SVM, while Random Forest decreases when the abalone dataset is applied to data scaling. Random Forest has the highest average balanced accuracy (74.87%) without data scaling.
Optimasi SVM menggunakan algoritme grid search untuk identifikasi citra biji kopi robusta berdasarkan circularity dan eccentricity Herlin Apriani; Jajam Haerul Jaman; Riza Ibnu Adam
Jurnal Teknologi dan Sistem Komputer Volume 10, Issue 1, Year 2022 (January 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2021.13807

Abstract

Varietas kopi merupakan salah satu faktor utama yang mempengaruhi kualitas dan harga kopi, sehingga penting untuk mengenali varietas kopi. Kajian ini bertujuan untuk optimasi pengenalan citra biji kopi robusta berdasarkan fitur circularity dan eccentricity menggunakan support vector machine (SVM) dan algoritme grid search. Metode yang digunakan terdiri dari, akusisi citra, preprocessing, ekstraksi fitur, klasifikasi, dan evaluasi. Circularity dan eccentricity digunakan dalam proses ekstraksi fitur, Sedangkan algoritme grid search digunakan untuk optimasi parameter SVM dalam proses klasifikasi pada 4 kernel berbeda. Kajian ini menghasilkan model klasifikasi terbaik dengan akurasi tertinggi sebesar 94% pada kernel RBF dan Polynomial.
Comparison of various epidemic models on the COVID-19 outbreak in Indonesia Intan Nuni Wahyuni; Ayu Shabrina; Inna Syafarina; Arnida Lailatul Latifah
Jurnal Teknologi dan Sistem Komputer Volume 10, Issue 1, Year 2022 (January 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2021.14222

Abstract

This paper compares four mathematical models to describe Indonesia's current coronavirus disease 2019 (COVID-19) pandemic. The daily confirmed case data are used to develop the four models: Logistic, Richards, SIR, and SEIR. A least-square fitting computes each parameter to the available confirmed cases data. We conducted parameterization and sensitivity experiments by varying the length of the data from 60 until 300 days of transmission. All models are susceptible to the epidemic data. Though the correlations between the models and the data are pretty good (>90%), all models still show a poor performance (RMSE>18%). In this study case, Richards model is superior to other models from the highest projection of the positive cases of COVID-19 in Indonesia. At the same time, others underestimate the outbreak and estimate too early decreasing phase. Richards model predicts that the pandemic remains high for a long time, while others project the pandemic will finish much earlier.
TATOPSIS: A decision support system for selecting a major in university with a two-way approach and TOPSIS Dewi Wardani; Widyaswari Mahayanti; Haryono Setiadi; Maria Ulfa; Endar Wihidayat
Jurnal Teknologi dan Sistem Komputer Volume 10, Issue 1, Year 2022 (January 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2021.14074

Abstract

Several problems can occur when students feel they have made the wrong choice of major in university. Choosing a major is one of the problems that students often face. Therefore, this study aims to develop a Decision Support System (DSS) to help students find majors that match their interests and abilities. This DSS proposes a two-way approach by considering students and the major's requirements, standards, and characteristics. The DSS utilizes the TOPSIS method; therefore, it is called TATOPSIS, which stands for Two-way Approach TOPSIS. It showed that the two-way approach in Scenario 1 (without score normalization) and Scenario 3 (with score normalization) shows better agreement results in 78.33% and 73.33% than the two-way approach for Scenario 2, Scenario 4, and the student-one-way approaches.
Pengelompokan wilayah menurut potensi ekonomi menggunakan modifikasi algoritme fuzzy k-prototypes untuk penentuan target pembangunan desa Hermawan Prasetyo
Jurnal Teknologi dan Sistem Komputer Volume 10, Issue 1, Year 2022 (January 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2021.14247

Abstract

Pengelompokan wilayah berdasarkan potensi ekonomi dapat dilakukan dengan klasterisasi data yang beratribut campuran, yaitu terdiri dari data numerik dan kategorik. Penelitian ini bertujuan untuk melakukan pengelompokan desa menurut potensi ekonomi dalam menentukan target pembangunan desa di Kabupaten Demak. Klasterisasi dilakukan dengan algoritme fuzzy k-prototypes dan modifikasi jarak Eskin untuk mengukur jarak atribut kategorik. Data yang digunakan adalah data PODES2018 dan Pemetaan Wilkerstat 2019. Klasterisasi desa menghasilkan tiga klaster desa menurut potensi ekonominya, yaitu klaster ekonomi rendah, sedang, dan tinggi. Klaster potensi ekonomi tinggi berada pada jalur transportasi utama Semarang–Kudus dan Semarang–Grobogan. Namun, desa-desa yang berada pada jalur utama transportasi tersebut masih ada yang masuk dalam klaster ekonomi rendah. Dengan mempertimbangkan status klasifikasi perkotaan/perdesaan desa, sebagian besar desa tersebut termasuk dalam kategori desa perkotaan. Hasil klasterisasi ini dapat dijadikan pedoman dalam menentukan target pembangunan desa dalam meningkatkan Indeks Desa Membangun di Kabupaten Demak.
Klasifikasi penerima bantuan program rehabilitasi rumah tidak layak huni menggunakan algoritme K-Nearest Neighbor An-Naas Shahifatun Na’iema; Harminto Mulyo; Nur Aeni Widiastuti
Jurnal Teknologi dan Sistem Komputer Volume 10, Issue 1, Year 2022 (January 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2021.14110

Abstract

The registrars for rehabilitation programs for uninhabitable settlements are increasing every year. The large data processing of registrants may result in inaccuracies and need a long time to determine livable houses (RTLH) and unfit for habitation (non RTLH). This study aims to apply the K-Nearest Neighbor algorithm in classifying the eligibility of recipients of uninhabitable house rehabilitation assistance. The data used in this study were 1289 data with 13 attributes from the Jepara Regency Public Housing and Settlement Service. Data processing begins with attribute selection, categorization, outlier data cleaning, and data normalization and method application. The proposed system has the best classification at k of 5 with an accuracy of 97.93%, 96.88% precision, 99.53% recall, and an AUC value of 0.964.
Sistem pengukuran ketinggian air sungai berbasis deteksi tepi Sobel Faiz Miftakhur Rozaqi; Wahyono Wahyono
Jurnal Teknologi dan Sistem Komputer Volume 10, Issue 1, Year 2022 (January 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2021.14119

Abstract

Flood is a natural disaster that often occurs in Indonesia. Therefore, a flood warning system is required to reduce the number of losses due to flooding. In this study, a Sobel edge detection-based framework is proposed to measure the river water level, which is expected to be used as an early flood warning system. Sobel edge detection is used to determine the edge of the water surface, which is then taken by the position of the pixels, and the height is calculated by comparing the image with actual conditions. The test results of the system implemented on the prototype show that this system has an RMSE less than 0.6986 mm and can run at 12 fps which in the future can be implemented directly on rivers.
Prediksi interaksi protein-protein berbasis sekuens protein menggunakan fitur autocorrelation dan machine learning Syahid Abdullah; Wisnu Ananta Kusuma; Sony Hartono Wijaya
Jurnal Teknologi dan Sistem Komputer Volume 10, Issue 1, Year 2022 (January 2022)
Publisher : Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jtsiskom.2021.13984

Abstract

Protein-protein interaction (PPI) can define a protein's function by knowing the protein's position in a complex network of protein interactions. The number of PPIs that have been identified is relatively small. Therefore, several studies were conducted to predict PPI using protein sequence information. This research compares the performance of three autocorrelation methods: Moran, Geary, and Moreau-Broto, in extracting protein sequence features to predict PPI. The results of the three extractions are then applied to three machine learning algorithms, namely k-Nearest Neighbor (KNN), Random Forest, and Support Vector Machine (SVM). The prediction models with the three autocorrelation methods can produce predictions with high average accuracy, which is 95.34% for Geary in KNN, 97.43% for Geary in RF, and 97.11% for Geary and Moran in SVM. In addition, the interacting protein pairs tend to have similar autocorrelation characteristics. Thus, the autocorrelation method can be used to predict PPI well.

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