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Ismail Setiawan
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INDONESIA
Jurnal Riset Sistem dan Teknologi Informasi
Published by Universitas Aisyiyah Surakarta
ISSN : -     EISSN : 29885663     DOI : https://doi.org/10.30787/restia
Core Subject : Science,
Computer Science & IT Decision Sciences, Operations Research & Management
theory and information science, information systems, information security, data processing and structure, programming and computing, software engineering, informatics, computer science, computer engineering, architecture and computer networks, robotics, parallel and distributed computing, operating systems, compilers and interpreters, games, numerical methods, mobile computing, natural language processing, data mining, cognitive systems, speech processing, machine learning, artificial intelligence, expert systems, geographical information systems, computational theory, and informatics applications in various fields.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 45 Documents
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Sistem Pendukung Keputusan untuk Pemilihan Sepeda Motor bagi Mahasiswa dengan Menggunakan Metode Simple Additive Weighting (SAW) Anggit Suryan Rohyan; Satria Fajar Dwi Kurniawan; Hendra Maulana; Nor Anisa
Jurnal Riset Sistem dan Teknologi Informasi Vol. 3 No. 2 (2025): Jurnal Riset Sistem dan Teknologi Informasi (RESTIA)
Publisher : Universitas Aisyiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30787/restia.v3i2.2067

Abstract

Researchers conducted a selection process for motorcycles that align with the needs and financial conditions of students, as this process is often quite complex. This complexity arises from the wide range of options and the numerous criteria that must be considered, such as price, fuel efficiency, engine capacity, comfort level, and design aesthetics. The objective of this study is to design a Decision Support System (DSS) by implementing the Simple Additive Weighting (SAW) method to assist students in selecting the motorcycle that best fits their needs. The SAW method was chosen due to its effectiveness in handling multi-criteria decision-making problems by assigning weights to each criterion and calculating the preference value for each available alternative. The system was developed using a quantitative approach, with data collected through surveys and documentation of motorcycle specifications. The test results indicated that the system was capable of providing accurate and relevant recommendations based on user needs. Therefore, this system has the potential to serve as an effective tool in supporting students' motorcycle selection decisions.
Perancangan Sistem Drone Pertanian Berbasis Tata Kelola TI dengan Pendekatan TOGAF Pramurwitasari, Anugrahaningtyas; Khoiru Nisa, Maulida; Malikul Rahman, Sigmawan; Muhammad Fadhil, Taqy; Najich, M. Narjun
Jurnal Riset Sistem dan Teknologi Informasi Vol. 4 No. 1 (2026): Vol. 4 No. 1 (2025): Jurnal Riset Sistem dan Teknologi Informasi (RESTIA)
Publisher : Universitas Aisyiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30787/restia.v4i1.2072

Abstract

Agricultural modernization in Indonesia faces challenges such as labor constraints, fertilizer efficiency, and low adoption of information systems. Agricultural drone technology offers innovative solutions, but structured implementation based on IT governance remains limited. This study used a descriptive qualitative approach to design a drone system based on the TOGAF ADM enterprise architecture. Data were collected through observations, semi-structured interviews, technical documentation, and a survey of 25 farmers in Andong District, Boyolali Regency. The system was designed by mapping the business architecture, application, data, and technology domains, and validated through surveys and expert discussion forums. The results showed that the modular and integrated drone system aligned with business objectives; the survey revealed that 80% of farmers experienced difficulties with manual fertilization, 88% were interested in using drones, and the average digital readiness score was 3.24/5. The drone is equipped with GPS, NDVI sensors, fertilizer tanks, a dashboard, and a mobile app for control and monitoring. The TOGAF approach has been proven to provide a comprehensive structure, supporting technology integration, IT governance, and sustainable implementation, making this system effective in improving efficiency, fertilization accuracy, and readiness for digital technology adoption.
Bapak Analisis dan Prediksi Diabetes Menggunakan Artificial Neural Network dengan Dataset CDC Diabetes Health Indicators : Analisis dan Prediksi Diabetes Menggunakan Artificial Neural Network dengan Dataset CDC Diabetes Health Indicators Dwi, Dodi Dwi Riskianto; Afandi, Muhammad; Ramadhan, M. Raihan; Sudriyanto, Sudriyanto
Jurnal Riset Sistem dan Teknologi Informasi Vol. 4 No. 1 (2026): Vol. 4 No. 1 (2025): Jurnal Riset Sistem dan Teknologi Informasi (RESTIA)
Publisher : Universitas Aisyiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30787/restia.v4i1.2096

Abstract

Diabetes mellitus is a chronic disease with increasing prevalence and requires effective early detection efforts. This study aims to develop a diabetes risk prediction model using an Artificial Neural Network (ANN) based on non-laboratory health indicators. The dataset used is the CDC Diabetes Health Indicators with a large amount of data and characteristics of classes that are not fully balanced. The research stages include data preprocessing that includes handling missing values, encoding categorical data using one-hot encoding, normalization of numerical features, and analysis of the target class distribution. The ANN model was trained using a Multilayer Perceptron architecture with dropout regularization and L2 penalty and AdamW optimization. The evaluation results show that the model achieved an accuracy of 86.45%, a precision of 85.2%, a recall of 82.7%, and an AUC-ROC value of 0.89. Although the accuracy is in the medium range for a large dataset, the high AUC value indicates excellent model discrimination ability. This performance is affected by the limited number of non-laboratory features used and the imbalanced class distribution. The findings of this study indicate that ANN based on simple health indicators has the potential to be used as a diabetes risk screening tool in primary healthcare. Further research is recommended to apply class balancing techniques, model interpretability analysis, and external validation in the Indonesian population.
Bapak Prediksi Depresi Mahasiswa Menggunakan Algoritma Random Forest Berbasis Data Psikososial Depression Prediction Among University Students Using a Random Forest Algorithm Based on Psychosocial Data : Prediksi Depresi Mahasiswa: Pendekatan Berbasis Data Psikososial Menggunakan Algoritma Random Forest Abiyya, Abiyya Alfahrizi Putra Arifiansyah; Afandi, Muhammad; Dwi Riskianto, Dodi; Sudriyanto, Sudriyanto
Jurnal Riset Sistem dan Teknologi Informasi Vol. 4 No. 1 (2026): Vol. 4 No. 1 (2025): Jurnal Riset Sistem dan Teknologi Informasi (RESTIA)
Publisher : Universitas Aisyiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30787/restia.v4i1.2100

Abstract

College students' mental health is a critical issue that is gaining increasing attention, particularly regarding depression, which significantly impacts quality of life and academic achievement. This study aims to develop a predictive model for depression in college students based on psychosocial data using the Random Forest algorithm. The data used is a public secondary dataset from Kaggle with 1,000 samples, covering demographic variables, lifestyle, and psychological indicators. The analysis process included data preprocessing, class balancing, model training, and evaluation using accuracy, precision, recall, F1-score, and confusion matrix metrics. Test results showed that the Random Forest model was able to predict depression with 87.0% accuracy, 86.1% precision, 87.4% recall, and 86.7% F1-score, demonstrating good and stable performance. Word cloud visualization identified academic pressure, stress, and anxiety as dominant factors. Compared to previous research using the SVM algorithm, Random Forest demonstrated improved performance, particularly in handling complex and imbalanced data. This study confirms the effectiveness of the Random Forest-based machine learning approach in supporting the early detection of college students' depression and provides a foundation for the development of mental health monitoring systems in higher education settings.
Sistem Pendukung Keputusan Diagnosa Penyakit Diabetes Menggunakan Metode Simple Additive Weighting (Saw) Oka dewata Syaputra; Zaehol fatah
Jurnal Riset Sistem dan Teknologi Informasi Vol. 4 No. 1 (2026): Vol. 4 No. 1 (2025): Jurnal Riset Sistem dan Teknologi Informasi (RESTIA)
Publisher : Universitas Aisyiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30787/restia.v4i1.2234

Abstract

Diabetes is a chronic metabolic condition marked by elevated blood glucose levels, necessitating early identification to avert long-term consequences, including cardiovascular diseases and organ impairment. The main obstacles in conventional diagnosis include time-consuming processes and limited medical experts, particularly in remote areas. This research aims to develop a web-based decision support system (DSS) to assist in the early diagnosis of diabetes by applying the Simple Additive Weighting (SAW) method. The developed system analyzes eight patient medical criteria: pregnancy count, level of sugar in the bloodstream, lower arterial pressure value, measurement of the triceps subcutaneous layer, amount of insulin present in the serum, and the body weight-to-height ratio index, genetic predisposition to diabetes, and age of the individual. Implementation and validation results show that the system successfully classified diabetes risk into three categories (low, medium, high) with 100% accuracy based on the comparison between system calculations and manual calculations. The system also features risk visualization based on a progress bar and automatic notifications triggered after medical personnel confirmation. In conclusion, this SAW-based DSS proves effective as an accurate and efficient screening tool for medical personnel in conducting early diabetes diagnosis, while potentially reducing healthcare service disparities in remote areas.

Page 5 of 5 | Total Record : 45

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