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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL REKAYASA TEKNOLOGI INFORMASI SINTECH (Science and Information Technology) Journal Jurnal Sisfokom (Sistem Informasi dan Komputer) Prosiding SAKTI (Seminar Ilmu Komputer dan Teknologi Informasi) METIK JURNAL Just TI (Jurnal Sains Terapan Teknologi Informasi) TEPIAN Buletin Poltanesa Jurnal Info Sains : Informatika dan Sains JSIKTI (Jurnal Sistem Informasi dan Komputer Terapan Indonesia) ETAM: Jurnal Pengabdian kepada Masyarakat Adopsi Teknologi dan Sistem Informasi
Muslimin B, Muslimin
Politeknik Pertanian Negeri Samarinda
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Sistem Pendukung Keputusan Kelompok pemilihan Saham LQ45 dengan menggunakan metode AHP, Promethee dan BORDA Mauko, Arfan; B, Muslimin; Sugiartawan, Putu
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 1 No 1 (2018): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (455.694 KB) | DOI: 10.33173/jsikti.6

Abstract

Investments shares on the Indonesia Stock Exchange is one of the investment with a high rate of return. Stock investment profit greatly influenced by the selection of the right stocks in a portfolio. Analyzing the uncertainty of a stock investor can involve the process of stock selection in group decision which includes investors, investment bankers, analysts, and brokers. Stock selection as a group can produce a stock portfolio with a higher rate of profit than the results of individual decision-making. Implementation of stock selection in group decision support systems (GDSS) used two economic approaches, namely fundamental analysis, and technical analysis. Fundamental analysis uses data financial ratios which have a significant influence on the development of a company's stock. Technical analysis is a stock valuation based on stock movement data time series. This research using AHP, PROMETHEE, and Borda to accommodate the results of shares in group decision making. This research resulted in ranking stocks as a group that can serve as recommendations for investors stock picking.
Perancangan aplikasi multi criteria decision making dalam penerimaan beasiswa kepada dosen studi lanjut STMIK Balikpapan menggunakan metode SAW B, Muslimin; Sumardi, Sumardi
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 1 No 1 (2018): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (383.931 KB) | DOI: 10.33173/jsikti.10

Abstract

Interests and number of STMIK Balikpapan new student enrollments are increasing every year. The balance of the ratio of lecturers to students is one of the most important components in improving the quality and teaching and learning process of a university. Avoiding shortages in the number of lecturers can be realized by providing scholarship programs to alumni and teaching assistants. This study aims to build a multi criteria decision making application that can assist the Head of HRD in the process of receiving scholarships to advanced and effective study lecturers. The multi criteria decision making application developed in this study uses the SAW method. The implementation of the SAW method includes the process of evaluating the weighting of criteria, evaluating alternative weights, the matrix process, the results of decision making preferences, resulting in the weighting and ranking of each alternative candidate for the scholarship recipient. The results of the evaluation of multi-criteria application decision making in the study are expected to produce modeling with a high degree of accuracy. The results of the analysis carried out can provide alternative recommendations for prospective scholarship recipients to advanced study lecturers in STMIK Balikpapan.
Perancangan Aplikasi Multi Criteria Decision Making Dalam Penerimaan Beasiswa Kepada Dosen Studi Lanjut STMIK Balikpapan Menggunakan Metode SAW B, Muslimin; Sumardi, Sumardi
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 2 No 4 (2020): June
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (818.951 KB) | DOI: 10.33173/jsikti.86

Abstract

Interests and number of STMIK Balikpapan new student enrollments are increasing every year. The balance of the ratio of lecturers to students is one of the most important components in improving the quality and teaching and learning process of a university. Avoiding shortages in the number of lecturers can be realized by providing scholarship programs to alumni and teaching assistants. This study aims to build a multi criteria decision making application that can assist the Head of HRD in the process of receiving scholarships to advanced and effective study lecturers. The multi criteria decision making application developed in this study uses the SAW method. The implementation of the SAW method includes the process of evaluating the weighting of criteria, evaluating alternative weights, the matrix process, the results of decision making preferences, resulting in the weighting and ranking of each alternative candidate for the scholarship recipient. The results of the evaluation of multi-criteria application decision making in the study are expected to produce modeling with a high degree of accuracy. The results of the analysis carried out can provide alternative recommendations for prospective scholarship recipients to advanced study lecturers in STMIK Balikpapan
Hypertension Risk Prediction Using GRU-Based Neural Network with Adam Optimization B, Muslimin; Racmadhani, Budi; Rudito, Rudito
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 2 (2023): December
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.258

Abstract

Hypertension remains one of the most prevalent chronic conditions worldwide and continues to be a major contributor to cardiovascular morbidity and mortality. Early identification of individuals at high risk is essential, yet conventional screening approaches often rely on periodic clinical examinations that may overlook subtle lifestyle or behavioral indicators. This study aims to address this challenge by developing a predictive model that estimates hypertension risk using a GRU-based neural network enhanced with the Adam optimization algorithm. The motivation for using this approach stems from the ability of GRU networks to capture nonlinear feature interactions and the effectiveness of Adam in improving training stability and convergence. The proposed system incorporates a structured preprocessing pipeline, feature scaling, and a sequential model architecture to classify individuals into hypertension and non-hypertension groups. The results show that the model achieves strong predictive performance, supported by accuracy trends, loss reduction patterns, and confusion matrix analysis that collectively demonstrate consistent learning behavior. The evaluation indicates that the GRU classifier successfully recognizes relevant health attributes such as stress levels, salt intake, age, sleep duration, and heart rate. Future research may explore expanded datasets, additional health indicators, or hybrid architectures to further enhance accuracy and improve clinical applicability. Overall, this work contributes an interpretable and efficient approach for health risk prediction and supports the development of intelligent digital health monitoring systems.
KNN-Based Prediction Model for Assessing Hypertension Risk from Lifestyle Features B, Muslimin; Rowa, Heruzulkifli
Jurnal Sistem Informasi dan Komputer Terapan Indonesia (JSIKTI) Vol 6 No 1 (2023): September
Publisher : INFOTEKS (Information Technology, Computer and Sciences)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33173/jsikti.265

Abstract

Hypertension is one of the most common chronic conditions associated with serious cardiovascular complications, and its prevalence continues to rise due to the influence of lifestyle related factors, motivating the use of data driven approaches for early risk identification. Although various machine learning models have been applied in health analytics, many still face challenges in processing heterogeneous lifestyle attributes, which limits their ability to accurately detect individuals at risk. This study addresses that gap by implementing the K Nearest Neighbors algorithm to predict hypertension using a dataset of 1,985 records containing variables such as age, salt intake, stress score, sleep duration, body mass index, family history, medication use, physical activity, and smoking status. The motivation for selecting KNN lies in its simplicity, adaptability, and strong performance in classification tasks involving structured health data. The contribution of this research includes the development of a lifestyle based hypertension prediction model supported by a preprocessing pipeline and optimized hyperparameters, enabling effective handling of mixed numerical and categorical features. The model is evaluated using accuracy, precision, recall, f1 score, and confusion matrix visualization, achieving an accuracy of 85 percent with balanced performance across both classes, showing that KNN offers reliable generalization for this dataset. Future work involves comparing KNN with ensemble or deep learning models, exploring feature selection techniques, and expanding dataset diversity to improve model robustness and applicability for real world digital health solutions.
Co-Authors Afak, Richa Rachmawati Andarias Liku annafi franz, annafi Annafi’ Franz Bin Idris, Nasruddin Eko Junirianto Husmul Beze, Husmul Ida Maratul Khamidah Imron Jamal Jamal Jamal Jamal Karim, Syafei Maria, Eny Mauko, Arfan Moch. Rizky Yuliansyah Moch. Rizky Yuliansyah Noviana Nurhuda, Asep Puput Misliyana PUTU SUGIARTAWAN Rachmadani, Budi Racmadhani, Budi Rahmawati Riza Putra, Emil Rowa, Heruzulkifli Rudito Rudito Rudito Selvia Rani Sri Murdhani, I Dewa Ayu Suci Ramadhani Suci Ramadhani Suci Ramadhani Suci Ramadhani Suci Ramadhani, Suci Sumardi . Suswanto Suswanto Taruk, Medi Wahyuni Eka Sari Yuanita Yuanita Yuliyana Yuliyana Yunike Andrayani Yunike Andrayani