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All Journal TEKNIK Jurnal Teknologi dan Manajemen Informatika Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL LENTERA : Kajian Keagamaan, Keilmuan dan Teknologi JOURNAL OF APPLIED INFORMATICS AND COMPUTING Dinamisia: Jurnal Pengabdian Kepada Masyarakat Jurnal Sisfokom (Sistem Informasi dan Komputer) Technomedia Journal Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) Capture : Jurnal Seni Media Rekam Jambura Journal of Informatics Building of Informatics, Technology and Science Jurnal ABDINUS : Jurnal Pengabdian Nusantara Jurnal Teknik Informatika (JUTIF) Business Economic, Communication, and Social Sciences Journal (BECOSS) Jurnal ABDIMAS (Pengabdian kepada Masyarakat) UBJ Jurnal Pendidikan dan Teknologi Indonesia Jurnal Locus Penelitian dan Pengabdian Eduvest - Journal of Universal Studies Jurnal Indonesia : Manajemen Informatika dan Komunikasi The Indonesian Journal of Computer Science
Santoso, Handri
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Religion Aerospace Engineering Arts Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Neuroscience Social Sciences Other

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Machine Health in a Click: A Website for Real-Time Machine Condition Monitoring Rochadiani, Theresia Herlina; Santoso, Handri; Aprilia, Novia Pramesti; Laurenso, Justin; Suhandi, Vartin
The Indonesian Journal of Computer Science Vol. 12 No. 6 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i6.3592

Abstract

Globalization in the current digital era has made it easier to use information technology to obtain fast and accurate information. One source of information is a website that can be used to monitor machine conditions in the industry. A good machine maintenance strategy is needed to maintain and increase machine productivity. Therefore, this research aims to build a website to monitor machine conditions in real-time. The machine condition is monitored using sushi sensors to track parameters such as temperature, acceleration, and velocity. Deep learning analysis is then used to identify anomalies in the machine. Using the SCRUM method, this website was successfully built. From the results of tests carried out using unit testing and integrated testing, every feature on this website can run well and according to user needs.
Contextual Smart School Architecture Integrating SERI and TIER for Digital Transformation Sembiring, Agustinus; Santoso, Handri
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.15910

Abstract

The digital transformation of elementary education has become an inevitable demand in the era of the Fourth Industrial Revolution. Nevertheless, schools in non-metropolitan regions continue to face persistent challenges, including limited infrastructure, low technology penetration, and insufficient policy support. This study aims to design a contextual smart school architecture by integrating the Smart Education Readiness Index (SERI) and the Transformation Impact and Essential Readiness (TIER) framework. A descriptive–qualitative approach, supported by quantitative survey data from 40 educators and education personnel, was employed to assess institutional readiness and formulate strategic priorities. The SERI assessment revealed an average digital readiness score of 3.12 (scale 0–4), with four dominant dimensions: Teaching and Learning Process (3.45), Assessment (3.28), Innovative Analysis (3.21), and IR 4.0 Policy (3.30). These dimensions were further validated through a Prioritisation Matrix weighted at 60% for cost factors, 20% for key performance indicators, and 20% for contextual proximity. The findings emphasize that effective digital transformation must leverage local strengths, be aligned with global frameworks, and be implemented through structured strategies. The key contribution of this research lies in the proposal of a modular, integrated, and sustainable smart school architecture model that can be replicated nationally to bridge global standards with local realities. This study provides both theoretical insights and practical implications for policymakers and educational leaders seeking to advance equitable digital transformation in non-metropolitan schools.
Comparative Academic Performance Prediction in Primary Schools Using Linear Regression and Random Forest Sembiring, Agustinus; Santoso, Handri
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.15953

Abstract

Predicting academic performance is an important aspect of data-driven decision making in education, particularly in primary schools where early identification of learning difficulties is crucial. This study compares the performance of Linear Regression and Random Forest Regression models for predicting students’ academic performance using an Educational Data Mining approach. The experiment uses the Students Performance Dataset from Kaggle, consisting of 1000 student records with eight predictor variables, including demographic and learning-related attributes. The target variable is the average score derived from mathematics, reading, and writing results. Model development and evaluation are conducted using Python in Google Colaboratory. Performance is assessed using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the coefficient of determination (R²), while Random Forest is further optimized using GridSearchCV with 5-fold cross-validation. The results show that Linear Regression achieves the best performance (R² = 0.162, RMSE = 13.40, MAE = 10.49), outperforming both the default Random Forest (R² ≈ 0.000) and the tuned Random Forest (R² ≈ 0.112). Although the explained variance is relatively low, this finding indicates that simple demographic features provide limited predictive power for academic performance. A case study using a local dataset from a private primary school involving 132 sixth-grade students further confirms that Linear Regression performs more effectively than Random Forest for small and simple educational datasets. These results highlight the importance of aligning model selection with dataset characteristics in educational data mining.
The Mapping Elementary School Digital Transformation Readiness through SERI for Roadmap Development Silalahi, Sondius Matogu Budiman; Santoso, Handri
Sinkron : jurnal dan penelitian teknik informatika Vol. 10 No. 2 (2026): Article Research April, 2026
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v10i2.15994

Abstract

Digital transformation has become a strategic priority in elementary education as schools are increasingly expected to integrate digital technology into teaching, assessment, and institutional management. However, previous studies on school digital readiness have generally focused on isolated aspects such as infrastructure, digital literacy, or leadership, without providing an integrated assessment model that simultaneously evaluates process, technology, and organisational dimensions in elementary school contexts. This study aims to assess the digital transformation readiness of an elementary school using the Smart Education Readiness Index (SERI). A descriptive quantitative case-study approach was employed by adapting the SERI assessment matrix into the elementary school context. The assessment covered three dimensions process, technology, and organisation through twelve indicators. Data were collected through a structured assessment matrix, supporting document review, and expert validation involving two educational technology experts. The results indicate that the school reached a moderate level of digital transformation readiness. The strongest indicators were specific or specialised skills (2.635), digital infrastructure readiness (2.634), digital interconnectivity (2.598), and organisational planning indicators (2.562), while the weakest indicators were assessment (1.708), policy guidance (1.708), general or transversal skills (1.744), and digital storage (1.852). Unlike previous studies that mainly assess digital readiness through separate technological or pedagogical indicators, this study applies a multidimensional institutional assessment framework. This study contributes by proposing a structured and adaptable assessment approach for elementary school digital transformation that supports the development of a more measurable and context-sensitive digital transformation roadmap.
Co-Authors Agus Anwar Akbar, Achmad Fathurrazi Akbariani, Nurhaliza Vania Amanda, Djaja Andrew Andrew, Andrew Andriani, Febri Anggraini, Astria Febrian Aprilia, Novia Pramesti Dazki, Erick Devanda, Faustin Eko Hadianto, Eko Firmansah, R Arif Fransisca, Viviana Harlis Setiyowati HARYONO Heni Jusuf Hindarto, Djarot Ho, Patricia Indrajit, Eko ISWAHYUDI ISWAHYUDI Ito Wasito Karjadi, Daniel Avian Laurenso, Justin Lijaya, Hendra Lucia Sri Istiyowati Lupitha, Mariska Mareta, Arvin Maria Magdalena Mayatopani, Hendra Mohammad, Saiful Azhari Muhammad Alfathan Harriz Nana Sutarna Panna, Ariya Perdana, Nanda Praharto, Suwandhy Prawira, Khairul Thamrin Prijantono, Sonny Priswanto, Budi Purwadi Purwadi Purwadi Putri, Gladys Rahman, Ben Ramadhoni, Moehammad Richardus Eko Indrajit Rochadiani, Theresia Herlina Ruddin, Isra Sakti, Irwin Sandjaya, Julio Santoso, Billy Cahyo Saputra, Adi Dwifana Sari, Muh Masri Sembiring, Agustinus Sentosa, Steve Silalahi, Sondius Matogu Budiman Sucipto, Nadya Rudie Suhandi, Vartin Suherman, Endang Sumargo, Ruly Sze, Edward Tauhid, Tauhid Wedha, Alessandro Enriqco Putra Bayu Wedha, bayu Yasa Widjaja, Herman Wijaya, Oktavianus Hendry