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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 5 Documents
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Search results for , issue "Vol. 3 No. 2 (2025): Jurnal Riset Sistem dan Teknologi Informasi (RESTIA)" : 5 Documents clear
Precision Healthcare: Leveraging Value Chain Analysis of Strategic Information Systems and Information Technology to Enhance Hospital Outcomes Kamarudin, Kamarudin
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.v3i2.1519

Abstract

In the healthcare digitalization era, leveraging strategic information systems (IS) and information technology (IT) through value chain analysis has emerged as a pivotal approach to enhance hospital outcomes. This study aims to develop a framework for integrating IS/IT strategies at Datu Sanggul Hospital, a Class C facility in South Kalimantan, to achieve precision healthcare delivery. The research methodology encompasses data collection via document studies, stakeholder interviews, and observations, coupled with rigorous data analysis and validation techniques. A strategic model formulates algorithms and system architectures guiding implementation of critical IS/IT initiatives like telemedicine, customer relationship management, and executive information systems. Robust IT strategies, including data center development, cloud computing, and disaster recovery planning, optimize operations and resource management. Mapping business needs to tailored IS solutions ensures precision across financial reporting, inventory management, employee training, and interdepartmental collaboration. The proposed approach aligns IS/IT strategies with operational objectives through value chain analysis, enhancing patient care quality, operational efficiency, and resource optimization. Positioning the institution for precision medicine, this framework drives innovation and improves health outcomes in healthcare's evolving landscape.
Klasifikasi Data Tak Seimbang menggunakan Algoritma Random Forest dengan SMOTE dan SMOTE-ENN (Studi Kasus pada Data Stunting) Fauziah, Anju; Julan Hernadi
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.v3i2.1906

Abstract

The random forest algorithm is one of the widely used machine learning classification methods because it has the advantage of reducing the risk of overfitting while improving general prediction performance. However, for data with unbalanced classes, this algorithm lacks to achieve its best performance, particularly in predicting data in the minority class. As a result, this article proposes two resampling approaches to balance the data: the Synthetic Minority Oversampling Technique (SMOTE) and the Synthetic Minority Oversampling Technique with Edited Nearest Neighbors (SMOTE-ENN). For the data classification technique, the random forest algorithm is applied to the original data, then to the resampling results using both SMOTE as well as SMOTE-ENN. The case study was applied to stunting data consisting of 421 cases in the majority class and 79 in the minority class. An accuracy of 89% was obtained on the original data, 90% on the resampled data with SMOTE-ENN, and 91% on the resampled data with SMOTE. The best accuracy was obtained using resampling technique with SMOTE, however it was not particularly significant.
Evaluasi Sentimen Pengguna ChatGPT Menggunakan Naive Bayes: Tinjauan dari Confusion Matrix dan Classification Report Dianda Rifaldi; Tri Stiyo Famuji; Bella Okta Sari Miranda; Fauzan Purma Ramadhan; Iriene Putri Mulyadi; Vanji Saputra6; Fanani, Galih Pramuja Inngam
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.v3i2.1990

Abstract

The development of artificial intelligence (AI) technology, particularly in natural language processing (NLP), has led to various innovations, including ChatGPT. Its growing popularity highlights the need for user sentiment analysis. This study evaluates user sentiment toward ChatGPT using the Naive Bayes algorithm. The dataset, obtained from Kaggle, consists of 500 labeled English tweets categorized as positive, neutral, or negative. The process involved text preprocessing, TF-IDF feature extraction, data splitting (80% training, 20% testing), and model training. The results show an accuracy of 56%, with the highest f1-score in the negative class (0.67) and the lowest in the neutral class (0.38). The model exhibits classification imbalance, with high precision but low recall in the neutral class, and high recall but low precision in the positive class. The confusion matrix further confirms frequent misclassifications between classes. These findings reflect the limitations of Naive Bayes in handling contextual relationships in text data. Improvements can be achieved through data balancing, enhanced NLP-based feature representation, and the application of more complex classification algorithms.
Metode Forward chaining untuk Deteksi Gangguan Kejiwaan Dini Bakhtiar, Muhammad Yusuf; Triyadi; Sihombing, Redo Abeputra; Fauzan Natsir
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.v3i2.1996

Abstract

Mental disorders are specific conditions associated with symptoms and pain that cause disruptions in psychosocial functioning. In general, people who want to diagnose mental disorders need to meet directly with a doctor. This research aims to develop an expert system that can assist in the process of diagnosing mental disorders, where this system can produce decisions equivalent to those of a doctor, so that the public no longer needs to meet a doctor directly for initial diagnosis. This research applies the forward chaining method with 5 types of disorders and 28 types of symptoms, which is a search technique that starts with known facts, then managed with existing data and applies inference rules to reach a conclusion. Thus, the application of this method has the potential to become an innovative solution in supporting the prevention and management of mental disorders from the early stages.
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.

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