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All Journal Jupiter Jurnal Teknologi Format : Jurnal Imiah Teknik Informatika JIKO (Jurnal Informatika dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING IJISCS (International Journal Of Information System and Computer Science) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Informasi Interaktif Jurnal Teknologi Komputer dan Sistem Informasi bit-Tech Respati Jurnal E-Komtek Teknika Jurnal Sosial dan Teknologi Jurnal Informatika Teknologi dan Sains (Jinteks) Tamansiswa International Journal in Education and Science (TIJES) Jurnal Pengabdian Masyarakat - Teknologi Digital Indonesia Journal of Intelligent Software Systems Jurnal Teknologi Informasi Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII Kohesi: Jurnal Sains dan Teknologi Jurnal Ilmiah Mahasiswa Agrokomplek Fahma : Jurnal Informatika Komputer, Bisnis dan Manajemen Teknomatika: Jurnal Informatika dan Komputer VISA: Journal of Vision and Ideas Jurnal Wicara Desa JTKSI (Jurnal Teknologi Komputer dan Sistem Informasi)
Femi Dwi Astuti
Universitas Teknologi Digital Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mechanical Engineering Neuroscience Physics Public Health Social Sciences Transportation Other

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Journal : bit-Tech

 1 
Implementasi ORESTE dengan Basson Rank untuk pemilihan tempat kursus TOEFL di Yogyakarta Femi Dwi Astuti; Siska Lidya Revianti
bit-Tech Vol. 6 No. 1 (2023): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v6i1.863

Abstract

TOEFL is one of the most frequently used international standardized tests for registering for masters and doctoral degrees, seeking study scholarships or for promotions. Unfortunately, there are still many students and employees who still experience difficulties in taking the TOEFL test. This is what then becomes the reason for them to take English courses, especially to prepare themselves for preparing the TOEFL test. Yogyakarta, which has the title of "Student City", has many campuses and many English courses that offer the TOEFL Preparation Course. There are many types of TOEFL formats and many TOEFL Preparation Programs are offered by English courses in Yoggyakarta. The TOEFL Preparation courses has various programs including different price, teacher competencies, and facilities. In this study, the ORESTE method was used to help the people determining the best course according to its ranking. This method is one of the Multiple Attribute Decision Making (MADM) methods used to find alternatives using predetermined criteria and preference weights. The criteria used are price, instructor, materials, facilities and location. The results showed that the best TOEFL course ranking/order alternative was A16 with a preference score of 3.9425 and the course with the lowest score was A4 with a score of 9.878177.
K-Means Approach to Identifying High-Risk Stunting Areas in Indonesia Hairul Aji; Femi Dwi Astuti
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3042

Abstract

Stunting is one of the chronic nutritional problems that remains a serious issue in Indonesia, particularly in Bulungan Regency, North Kalimantan. This condition not only affects children's physical growth but also their cognitive development, which has implications for their quality of life and future prospects. This study aims to classify regions based on stunting risk levels to provide a more targeted framework for local governments in setting intervention priorities. The method used is K-Means Clustering, an effective data mining algorithm for non-hierarchical data clustering. The data used are secondary data from the Bulungan District Bappeda in 2021, covering 81 locations with 29 stunting risk factor variables. The analysis process was conducted through data processing, centroid initialization, Euclidean distance calculation, and the formation of convergent clusters. The results of the study show the formation of two main clusters: a cluster with moderate vulnerability and a cluster with high vulnerability. The moderate cluster is in a transitional state with fluctuating risks, while the high cluster has low health and sanitation indicators, requiring special attention. These findings indicate that the K-Means method can provide data-driven insights to support stunting prevention policies. This study is expected to serve as a reference for local governments in developing more targeted intervention programs and contribute academically to the application of data mining methods in public health.
Predicting Moral Degradation Using Tree-Ensemble Machine Learning Methods Rindha Fajirulhabshah; Femi Dwi Astuti
bit-Tech Vol. 8 No. 3 (2026): bit-Tech - IN PROGRESS
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3514

Abstract

Moral degradation poses significant challenges across social, organisational, and digital environments, yet empirical tools for predicting individual vulnerability to unethical behaviour remain limited. This study develops an interpretable machine learning-based predictive model to estimate tendencies toward moral degradation using multidimensional moral domain scores derived from the Moral Perspectives and Foundations Scale (MPFS), with a specific focus on the Perpetrator Relevance (PR01) block. The final analytical sample consisted of 2,130 respondents after data filtering. Two tree-ensemble algorithms, Random Forest (RF) and Gradient Boosting (GB), were implemented and compared using an 80:20 train-test split. Model performance was evaluated using the coefficient of determination (R²), Mean Absolute Error (MAE), and Mean Squared Error (MSE). The results demonstrate that both models achieved strong predictive performance across all PR01 moral domains, with GB consistently outperforming RF. The highest predictive accuracy was observed in the Loyalty (GB R² = 0.616) and Authority (GB R² = 0.595) domains, accompanied by lower MAE and MSE values, indicating stable predictive tendencies rather than deterministic moral behaviour. To enhance interpretability, SHAP analysis was applied, revealing that binding moral dimensions, particularly Loyalty and Authority across multiple moral perspectives, exert the strongest influence on predicted moral degradation tendencies. Overall, the findings highlight the value of integrating ensemble learning with explainable AI techniques in moral psychology. Given the cross-sectional nature of the data, the proposed framework is intended as a risk-detection tool rather than a diagnostic or causal model, while future research should incorporate longitudinal and behavioural data to strengthen generalisability and inference.
Co-Authors Adiguna, I Gede Divbodhi Adiningtyas, Baiq Faras Sukma Agustinus Agustinus Andriyani, Widyastuti Arghyanita, Ni Luh Made Swasti Arrio Saputra Bagas Antuk Pramukti Bambang Budi Santoso Bambang Purnomosidi Dwi Putranto Danny Kriestanto Danny Kriestanto, Danny Dwi Noorma Putri Erranaomi, Muhammad Brilian Fajri, Ika Nur Fauzi, Arma Febri Nova Lenti Guntara, Mohammad Hairul Aji Hudaimi Husain, Nadia Salsabila I Komang Damar Jaya Indra Yatini Buryadi Indra Yatini Buryadi Jayaputra Maharani, Febi Maharani, Kadek Dyah Aurelia Nugroho, Muhammad Agung Nurwiyati, Fransisca Wiwiek Pius Dian Widi Anggoro Ponco Nugroho, Edo Septian Ramadhani, Nur Revianti, Siska Lidya Rikie Kartadie Rindha Fajirulhabshah Rosit Sanusi Salahuddin, Muamar Omar Salwa, Nabila Widhi Suharijanto, Richardo Amos Sumiyatun Syamsu Windarti Utami, Firmania Dwi Wahab, Alvionita Safira Wibowo, Dwi Winarno, Basuki Heri Wiwi Widayani, Wiwi Yogo Hermawan Yuli Astuti