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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of Information System KOMPUTIKA - Jurnal Sistem Komputer KOMPUTA : Jurnal Ilmiah Komputer dan Informatika Jurnal Tata Kelola dan Kerangka Kerja Teknologi Informasi Indonesian Community Service and Empowerment Journal (IComSE) Al-MIKRAJ: Jurnal studi Islam dan Humaniora Jurnal Pengabdian Pelitabangsa Jurnal Pengabdian Teknik dan Ilmu Komputer (PETIK) Indonesian Journal of Infomatics
Sri Nurhayati
Sistem Komputer, Fakultas Teknik Dan Ilmu Komputer, Universitas Komputer Indonesia
Religion Arts Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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The Effect of Data Imbalance on the Interpretation Stability of LIME-Based Explainable AI on Nutritional Status Prediction Models Sri Nurhayati; Hidayat Hidayat; Siti Ar-Rachmi Ningrum; Zainal Arifin Hasibuan; Sri Supatmi
Indonesian Journal of Infomatics Vol. 1 No. 2 (2026): May: Indonesian Journal of Infomatics
Publisher : Asosiasi Pengelola Jurnal Informatika dan Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.66472/iji.v1i2.438

Abstract

Data imbalance is a common challenge in nutritional status prediction because it can reduce classification performance and influence the reliability of Explainable Artificial Intelligence (XAI) interpretations. This study aims to examine the impact of data imbalance on the stability of Local Interpretable Model-Agnostic Explanations (LIME)-based interpretations. A Random Forest model was developed under two scenarios: using the original imbalanced dataset and using a balanced dataset generated through the Synthetic Minority Over-sampling Technique (SMOTE). Model performance was evaluated and compared, followed by LIME-based interpretation and stability analysis. The results indicate that SMOTE enhanced the model’s ability to identify minority classes, with recall increasing from 0.36 to 0.55, although overall accuracy slightly declined. LIME analysis revealed changes in feature contributions between the two scenarios, reflecting the influence of data distribution on model explanations. The interpretation stability score reached 0.80, suggesting relatively consistent explanations despite variations in class balance. These findings highlight the importance of jointly evaluating predictive performance and interpretation stability in health-related machine learning applications.
Application of the Machine Learning Method for Predicting International Tourists in West Java Indonesia Using the Averege-Based Fuzzy Time Series Model Sri Nurhayati; Syahrul Syahrul; Riani Lubis; Mochamad Fajar Wicaksono
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 1 (2023): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i1.25475

Abstract

The purpose of this study is to propose whether an average-based fuzzy time series model is appropriate for use in predicting the number of foreign tourists coming to West Java, Indonesia. Machine learning is a branch of artificial intelligence where machines are designed to learn on their own without human direction. One of the machine learning methods used by data science is for prediction processes, such as predicting the number of tourists. Tourism is one of the economic sectors that has a direct impact on the community's economy. Based on data from the Badan Pusat Statistik (BPS), the number of tourists coming to West Java Indonesia fluctuates, meaning that the number can increase and decrease every month and year. Changes in the number of tourists that fluctuate are one of the problems that have an impact on tourism actors. Therefore, the solution given to answer this problem is that an appropriate model is needed to predict the number of tourists visiting West Java. The contribution of this research is to help related parties in predicting the number of foreign tourists so that it can be used as one to make policies related to tourism preparation and planning efforts in West Java, Indonesia.  The method used in this research is a case study approach, where the case study is taken from data on foreign tourists visiting West Java from 2017 to 2020. For the prediction process, the method used is the fuzzy time series method and the average length-based algorithm as the determinant of the interval length. Effective interval length can affect prediction results with a higher level of accuracy. Based on the prediction test results, the Mean Absolute Percentage Error (MAPE) value is 14.71%. These results indicate that the fuzzy time series model based on the average interval length is good for prediction.
Interactive Solar System Learning Media Using the Raspberry Pi 3B Mochamad Fajar Wicaksono; Myrna Dwi Rahmatya; Syahrul Syahrul; Sri Nurhayati
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 9 No. 2 (2023): June
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i2.25948

Abstract

The current learning solar system process uses solar system props. This research aimed to create an interactive solar system learning tool for elementary school students. With this tool, students can learn about the planets in the solar system in learning mode. In addition, there is a question mode to test students' knowledge abilities. This research's contribution was to provide an engaging way for sixth-grade elementary school students to learn about and recognize the planets in the solar system. The method used in this research is the experimental method. The primary part of this system is the Raspberry Pi. In this tool, there are two modes: learning mode and question mode. The learning mode involves the input button for eclipse mode and the LDR sensor as a trigger for activating the DC motor and reading solar system material using gTTS. The question mode involves a question bank on the web application, gTTS for reading questions, and speech recognition for processing answers given by students.   The teacher can add, change, or delete questions and learning materials through the web application. The test on the learning tool is 100% successful. In learning mode, the device can read input from the LDR sensor and provide sound output, and in question mode, the device will ask questions, receive answers in voice form and then process the response based on program scenarios. On the other side, Based on UAT results from 20 sixth-grade elementary school student,  95.14% of student agreed that solar system learning media and quiz features make the learning process more engaging, easy to use, help students understand solar system material, and can be used as a learning tool.
Pelatihan Aplikasi Olah Data Penduduk untuk Meningkatkan Efektivitas Pengelolaan Data Penduduk di Desa Cihanjuang Bandung Barat Sri Nurhayati; Hani Irmayanti; Mochamad Fajar Wicaksono; Hidayat; Fariz Nugraha; Aditya Wandani
Jurnal Pengabdian Teknik dan Ilmu Komputer (Petik) PETIK : Jurnal Pengabdian Teknik dan Ilmu Komputer Vol. 5 No. 2 Desember 2025
Publisher : Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/petik.v5i2.17250

Abstract

Kebutuhan akan pengolahan data penduduk yang efisien menjadi semakin penting terutama bagi pengurus RT/RW yang berperan langsung dalam pelayanan masyarakat di tingkat lingkungan. Saat ini, pengelolaan data penduduk di tingkat RT/RW masih dilakukan secara manual melalui pencatatan dalam buku besar atau spreadsheet sederhana, sehingga rawan kesalahan dan kurang efisien. Kegiatan pengabdian ini bertujuan untuk meningkatkan efektivitas pengelolaan data penduduk melalui penerapan aplikasi pengolahan data berbasis web. Metode pelaksanaan meliputi penyampaian materi secara interaktif, praktik langsung penggunaan aplikasi, pendampingan teknis, serta evaluasi menggunakan kuesioner skala Likert yang diisi oleh lima peserta pelatihan. Hasil evaluasi menunjukkan tingkat kepuasan yang sangat tinggi dengan skor rata-rata 4,80 dari 5 (96%), yang menandakan bahwa pelatihan dan implementasi aplikasi mampu meningkatkan efisiensi dan akurasi pengelolaan data penduduk. Berdasarkan hasil tersebut, kegiatan ini disimpulkan berhasil mencapai tujuan dan direkomendasikan untuk dilanjutkan melalui pengembangan fitur lanjutan serta pendampingan berkala.
Pengembangan Model Tata Kelola Explainable AI pada Sistem Peringatan Dini Stunting : Pendekatan Socio-Technical dan Technology Acceptance Diana Effendi; Sri Nurhayati; Agus Nursikuwagus; Yeffry Handoko Putra; Rio Yunanto
Jurnal Tata Kelola dan Kerangka Kerja Teknologi Informasi Vol. 12 No. 2 (2026): Agustus 2026
Publisher : Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/jtk3ti.v12i2.19959

Abstract

This study develops a governance model for Explainable Artificial Intelligence (XAI) in a stunting early warning system by integrating a socio-technical approach and the Technology Acceptance Model (TAM). The main issues examined are the low transparency of AI systems, the lack of structure in health AI governance, and the need to build user trust before predictive systems are used in public health services. The research method employed a mixed-methods approach, consisting of a quantitative survey of 100 respondents and semi-structured interviews with 10 informants from the Health Department, Community Health Centers (Puskesmas), the Communication and Information Department (Diskominfo), midwives, and Posyandu cadres. Quantitative data were analyzed using multiple linear regression, while qualitative data were used to strengthen the socio-technical interpretation. The results indicate that XAI and governance have a positive influence on trust. Furthermore, trust and perceived usefulness have a positive influence on behavioral intention. The resulting model identifies transparency, accountability, security, compliance, periodic validation, audits, and feedback as governance mechanisms that link the technical quality of AI with user acceptance. The contribution of this research is a conceptual model of XAI governance that can serve as the basis for developing a transparent, accountable, and user-accepted early warning system for stunting. Keywords – Behavioral Intention; Explainable AI; Socio-Technical; Stunting; Governance.  
Symantic Literatur Review : Artificial Intelligence dalam Telemedicine dan Remote Patient Monitoring Diana Effendi; Sri Nurhayati; Zainal Arifin Hasibuan; Bobi Kurniawan S.; Sri Supatmi
Komputa : Jurnal Ilmiah Komputer dan Informatika Vol 15 No 1 (2026): Komputa : Jurnal Ilmiah Komputer dan Informatika
Publisher : Program Studi Teknik Informatika - Universitas Komputer Indonesia (UNIKOM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputa.v15i1.18702

Abstract

The development of telemedicine and Remote Patient Monitoring (RPM) is increasing along with the need for efficient, adaptive, and data-driven remote healthcare services. Artificial Intelligence (AI) plays a crucial role in strengthening these systems through predictive analysis, medical classification, and real-time patient monitoring. However, research on AI integration in telemedicine and RPM remains scattered and exhibits wide methodological variation, necessitating a systematic review to understand the consistency of findings and the direction of research development. This study conducted a Systematic Literature Review (SLR) following the PRISMA 2020 protocol, analyzing 128 publications from 2020–2025 obtained from Scopus, PubMed, IEEE Xplore, and Google Scholar. This study combined SLR synthesis with bibliometric mapping (co-occurrence and thematic mapping) to highlight the evolution of themes and topical interrelationships more explicitly. Bibliometric analysis results show an increase in the number of publications from 12 articles in 2020 to 45 articles in 2024, a nearly fourfold increase, before stabilizing in 2025. Co-occurrence and thematic mapping findings reveal four main themes: telemedicine–AI, computational methods based on machine learning and deep learning, physiological monitoring, and human factors in clinical evaluation. The study also identifies several challenges, including data security, signal quality, model transparency, and healthcare worker readiness. Theoretically, the findings emphasize that AI integration in telemedicine–RPM needs to be understood as a socio-technical issue that demands human-centered evaluation. Policy-wise, strengthening data governance and clinical validation standards is necessary for more accountable and secure implementation. This study concludes that AI plays a central role in the development of telemedicine and RPM, but further studies are needed on service personalization, multimodal data integration, and large-scale clinical validation.
Co-Authors Aditya Wandani Agus Nursikuwagus Andrias Darmayadi Bobi Kurniawan S. Diana Effendi Fariz Nugraha Hani Irmayanti Hidayat Hidayat Hidayat Hidayat, Hidayat Iman Immanudin Mochamad Fajar Wicaksono Myrna Dwi Rahmatya R H Nugraha Riani Lubis Rio Yunanto Siti Ar-Rachmi Ningrum Sri Supatmi Syahrul Syahrul Usep Mohamad Ishaq Wicaksono, Mochamad Fajar Yeffry Handoko Putra Zainal Arifin Hasibuan