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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Simetris PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Jurnal technoscientia Sinkron : Jurnal dan Penelitian Teknik Informatika JIKO (Jurnal Informatika dan Komputer) JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Review Pendidikan dan Pengajaran (JRPP) Jurnal Informatika dan Rekayasa Perangkat Lunak JURNAL TEKNOLOGI TECHNOSCIENTIA Jurnal Pendidikan dan Teknologi Indonesia Journal of Dinda : Data Science, Information Technology, and Data Analytics COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Journal of Computers and Digital Business Journal of Intelligent Software Systems Journal of Public Health Concerns Eduvest - Journal of Universal Studies Journal Of Artificial Intelligence And Software Engineering CYBER-Techn(Jurnal Informatika dan Industri) INOVTEK Polbeng - Seri Informatika BHAKTI JIVANA Quality
Andriyani, Widyastuti
Universitas Teknologi Digital Indonesia
Religion Aerospace Engineering Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Neuroscience Nursing Public Health Social Sciences Other

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Interpretable Rule-Based Clinical Decision Support for Early Screening Of Heart Disease using C4.5 Decision Trees Andriyani, Widyastuti; Wiyanti, Dian Tri; Nugroho, Daniel C.A.
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 1 (2026): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.112701

Abstract

This study develops an interpretable rule-based clinical decision support system for early screening of heart disease presence by integrating the C4.5 decision tree algorithm with a rule-based reasoning mechanism. The proposed approach is intended to assist clinicians in obtaining rapid, transparent preliminary indications from clinical data, particularly in settings that require lightweight and auditable solutions. The dataset was obtained from the UCI Heart Disease Repository and comprises 299 patient records, evaluated using a 70% training and 30% testing split in RapidMiner. Experimental results show that the C4.5 model achieves an accuracy of 86.52% and produces clinically interpretable IF–THEN rules, enabling traceable reasoning and decision auditing. Although C4.5 is a classical learning algorithm, it remains relevant for clinical decision support due to its auditability, low computational cost, and ease of deployment in resource-constrained environments. The developed system is expected to support early screening/triage and data-driven clinical decision-making, contributing to the advancement of medical decision support systems (MDSS).
Optimalisasi pengetahuan pola hidup sehat melalui gerakan masyarakat hidup sehat (GERMAS) Eny Retna Ambarwati; Reni Tri Lestari; Ivanna Beru Brahmana; Widyastuti Andriyani; Murgi Handari; Istichomah Istichomah; Agnes Erida Wijayanti; Riadinata Riadinata; Fika Pratiwi
JOURNAL of Public Health Concerns Vol. 6 No. 1 (2026): JOURNAL of Public Health Concerns
Publisher : Indonesian Public Health-Observer Information Forum (IPHORR) Kerja sama dengan: Unit Penelitian dan Pengabdian Kep Akademi Keperawatan Baitul Hikmah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56922/phc.v6i1.2692

Abstract

Background: Non-communicable diseases (NCDs) remain a major global public health challenge. Each year, approximately 41 million deaths, or nearly three-quarters of all global deaths, are caused by NCDs. Of these, approximately 17 million occur in people under 70 years of age, with the majority of cases (86%) occurring in low- and middle-income countries. This situation demands the strengthening of sustainable promotive and preventive strategies, one of which is through the implementation of the Healthy Living Community Movement.Purpose: Health promotion to increase knowledge, awareness, and community involvement in NCD prevention through the implementation of the Healthy Living Community Movement at the community level.Method: Community service was conducted at the Volleyball Court of RT 03, Sumbergamol Hamlet, Balecatur, Gamping, Sleman, Yogyakarta, involving 97 participants consisting of adolescents, fertile couples, and the elderly. The series of activities included joint physical activities such as healthy walks and exercise, free health check-ups for early detection, and education on the importance of consuming fruits and vegetables as part of a healthy lifestyle.Results: Observations showed an increase in core strength, improved joint and body mobility, participants were able to perform movements with balance and good coordination, and improved respiratory and circulatory systems. Most participants were able to follow the exercise movements correctly. The majority understood the importance of exercise for maintaining endurance and were motivated to develop healthy lifestyle habits consistently and independently.Conclusion: The community service program, focusing on the Healthy Living Community Movement, yielded positive results in increasing community awareness and participation in efforts to consistently and independently maintain and improve health. This activity also increased community knowledge about the importance of regular physical activity and daily consumption of fruits and vegetables for health.Suggestion: Empowerment of health cadres and community leaders, as well as synergy between village governments, health care facilities, educational institutions, and community organizations are expected to be the primary drivers in educating and motivating the public about adopting a healthy lifestyle. It is also hoped that with a shared commitment and strengthened cross-sector collaboration, the implementation of the Healthy Living Community Movement will have a broader and more sustainable impact on improving public health. Keywords: Health promotion; Healthy living community movement; Non-communicable diseases; Prevention Pendahuluan: Penyakit Tidak Menular (PTM) masih menjadi tantangan utama kesehatan masyarakat di tingkat global. Setiap tahun, sekitar 41 juta kematian atau hampir tiga perempat dari total kematian dunia disebabkan oleh PTM. Dari jumlah tersebut, sekitar 17 juta kematian terjadi pada usia kurang dari 70 tahun, dengan sebagian besar kasus (86%) ditemukan di negara berpendapatan rendah dan menengah. Kondisi ini menuntut penguatan strategi promotif dan preventif yang berkesinambungan, salah satunya melalui implementasi gerakan masyarakat hidup sehat (GERMAS).Tujuan: Promosi kesehatan guna meningkatkan pengetahuan, kesadaran, dan keterlibatan masyarakat dalam pencegahan PTM melalui penerapan GERMAS di tingkat komunitas.Metode: Pengabdian kepada masyarakat dilaksanakan di Lapangan Voli RT 03 Dusun Sumbergamol, Balecatur, Gamping, Sleman, Yogyakarta, dengan melibatkan 97 peserta yang terdiri atas remaja, pasangan usia subur, dan lansia. Rangkaian kegiatan meliputi aktivitas fisik bersama berupa jalan sehat dan senam, pemeriksaan kesehatan gratis sebagai langkah deteksi dini, serta penyuluhan mengenai pentingnya konsumsi buah dan sayur sebagai bagian dari pola hidup sehat.Hasil: Berdasarkan pengamatan, menunjukkan terdapat peningkatan kekuatan inti tubuh, peningkatan kemampuan gerak sendi dan tubuh, peserta mampu melakukan gerakan dengan seimbang dan koordinasi yang baik, perbaikan sistem pernapasan dan peredaran darah. Sebagian besar peserta mampu mengikuti gerakan senam dengan benar. Mayoritas peserta memahami pentingnya olahraga untuk menjaga daya tahan tubuh dan termotivasi untuk membangun kebiasaan hidup sehat secara konsisten dan mandiri.Simpulan: Program pengabdian masyarakat dengan fokus pada gerakan masyarakat hidup sehat (GERMAS) memberikan hasil yang positif dalam meningkatkan kesadaran dan partisipasi masyarakat terhadap upaya menjaga dan meningkatkan kesehatan secara konsisten dan mandiri. Kegiatan ini juga meningkatkan pengetahuan masyarakat mengenai pentingnya aktifitas fisik yang teratur dan mengkonsumsi buah dan sayur setiap hari terhadap kesehatan.Saran: Diharapkan, pemberdayaan kader kesehatan, tokoh masyarakat, serta sinergi antara pemerintah desa, fasilitas pelayanan kesehatan, institusi pendidikan, dan organisasi kemasyarakatan sebagai penggerak utama dalam mengedukasi dan memotivasi masyarakat mengenai penerapan pola hidup sehat. Diharapkan juga dengan komitmen bersama dan penguatan kolaborasi lintas sektor, implementasi GERMAS diharapkan mampu memberikan dampak yang lebih luas dan berkelanjutan terhadap peningkatan derajat kesehatan masyarakat.
OPTIMIZING MACHINE LEARNING PIPELINE DESIGN THROUGH PROGRAMMING PARADIGM SELECTION Kusjani, Adi; Andriyani, Widyastuti; Kristomo, Domy
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 2 (2026): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.112691

Abstract

This study investigates the impact of programming paradigm selection on the efficiency and sustainability of machine learning (ML) pipeline design. A case study was conducted using an agricultural IoT dataset for crop yield prediction, where four paradigms imperative, functional, object-oriented (OOP), and declarative were implemented to construct modular, maintainable, and reproducible pipelines. Each paradigm was evaluated through five key metrics: development time, debugging time, modularity, reproducibility, and maintainability. Experimental data were analyzed using descriptive statistics and visualized with boxplots and radar charts to identify performance differences. The results demonstrate that the functional paradigm achieved superior performance in data preprocessing with high reproducibility (95%), OOP produced the highest modularity (5.0/5), while the declarative paradigm exhibited the best reproducibility (98%) and deployment efficiency. In contrast, the imperative paradigm enabled faster prototyping but lacked long-term stability. Integrating paradigms in a multi-paradigm design reduced development time by 30.3%, debugging effort by 41.2%, and improved modularity and reproducibility by 41.6% and 21%, respectively. These findings highlight that no single paradigm is universally optimal; instead, a multi-paradigm approach provides a more efficient, maintainable, and production-ready ML pipeline framework adaptable to industrial-scale implementations.
Digital Transformation of Islamic Universities for Lecturer Performance Based on Decision Support System Nurohman, Muhamad; Andriyani, Widyastuti; Redjeki, Sri
QUALITY Vol 12, No 2 (2024): QUALITY
Publisher : Pascasarjana IAIN Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21043/quality.v12i2.30037

Abstract

Digital Transformation of Islamic Universities for Lecturer Performance Based on Decision Support System. The research offers the utilization of digital transformation along with the development of information and communication technology today needs to be done to improve the quality of education, especially in Islamic universities, one of which is the existence of various systems to support them with the existence of multiple kinds of systems to support decision making or Decision Support System. The system for determining outstanding lecturers is used to support learning and teaching activities on campus to create qualified and competent students in their fields. STAINU Purworejo is an Islamic university located in Central Java Province which has been established since 1974 and has 33 lecturers. The process of determining outstanding lecturers that is still running at STAINU Purworejo campus still has shortcomings, namely requiring a long time to process questionnaire data that has been distributed to the campus community and the decision results obtained are not fully valid and not objective. The determination of outstanding lecturers has five assessment criteria, namely: student assessment, superior assessment, educational qualifications, published journals, and lecturer administration. The method used is the Analytical Hierarchy Process (AHP) method to calculate the weight of each criterion and the Technique for Order by Similarity to Ideal Solution (TOPSIS) to rank alternatives based on each criterion. Based on calculations carried out using a combination of AHP and Topsis and system implementation, the highest preference value is 0.835 and the lowest preference value is 0.27. From the results of these calculations, it can be concluded that the utilization of digital transformation for lecturer performance with a Decision Support System that utilizes a combination of AHP and TOPSIS methods can be used to help provide recommendations in decision-making to determine the best lecturer at the Nahdlatul Ulama Islamic Religious College (STAINU) Purworejo.
Studi Eksploratif Pipeline Multilayer Perceptron pada Dataset Sintetik Berlabel Deterministik: Implikasi Metodologis untuk Klasifikasi Hipertensi Ivónia Fátima Ruas da Silva; Bambang Purnomosidi Dwi Putranto; Widyastuti Andriyani
Journal of Computers and Digital Business Vol. 5 No. 2 (2026)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v5i2.991

Abstract

Hipertensi merupakan penyakit kardiovaskular dengan prevalensi tinggi dan menjadi penyebab utama mortalitas global, sehingga deteksi dini menjadi kebutuhan klinis yang krusial. Namun, pengembangan model deep learning pada konteks sumber daya terbatas sering terkendala ketersediaan dataset berskala besar. Gap penelitian yang diidentifikasi adalah belum tersedianya studi eksploratif yang secara eksplisit menguji kelayakan pipeline Multilayer Perceptron (MLP) sederhana pada dataset berukuran sangat kecil dengan dokumentasi reproducible. Penelitian ini bertujuan mendemonstrasikan pipeline MLP end-to-end pada dataset sintetik 150 sampel dengan sembilan fitur biometrik dan gaya hidup. Setelah one-hot encoding dan normalisasi Min-Max, dimensi input menjadi 15 neuron. Arsitektur MLP terdiri atas tiga hidden layer (64-32-16, ReLU) dan output sigmoid, dilatih 100 epoch menggunakan optimizer Adam (learning rate 0,001; batch size 16) dengan early stopping. Evaluasi pada test set (n = 30) memperoleh akurasi 90,00%, presisi 85,00%, recall 100%, F1-score 91,90%, dan AUC-ROC 0,91, dengan tiga false positive teridentifikasi sebagai kasus borderline pre-hypertension. Kontribusi penelitian terletak pada penyajian artefak reproducible—dataset sintetik, kode preprocessing, dan visualisasi diagnostik—sebagai baseline pedagogis untuk institusi berketerbatasan data. Keterbatasan utama, yaitu sifat deterministik label yang berpotensi menimbulkan circular reasoning pada fitur tekanan darah, didokumentasikan eksplisit sebagai catatan validitas internal.
Evaluasi Multi-Dimensi Sebelas Model Pembelajaran Mesin untuk Klasifikasi Kekuatan Kata Sandi pada Lingkungan Sumber Daya Terbatas Muhammad Ali Sofian; Widyastuti Andriyani
Journal of Computers and Digital Business Vol. 5 No. 2 (2026)
Publisher : PT. Delitekno Media Madiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56427/jcbd.v5i2.998

Abstract

Kata sandi tetap menjadi mekanisme autentikasi paling banyak digunakan, namun mayoritas pelanggaran data tetap bersumber dari kredensial lemah. Studi ini mengevaluasi sebelas model pembelajaran mesin mencakup pendekatan klasik (Logistic Regression, Decision Tree, SVM, Random Forest) dan modern (XGBoost, LightGBM, CatBoost, Extra Trees, MLPClassifier, TabNet, Ensemble Stacking) pada dua dimensi evaluasi: kinerja prediktif dan efisiensi sistem. Dari korpus 1,6 juta kata sandi, diekstraksi sampel berimbang 30.000 entri (10.000 per kelas) dengan delapan fitur terinterpretasi. Temuan kritis adalah seluruh model mencapai akurasi mendekati 1,000 pada himpunan uji bukan bukti generalisasi, melainkan konsekuensi deterministik dari label berbasis aturan pada dataset Kaggle; sehingga sumbu pembeda model bergeser dari akurasi ke efisiensi. Distribusi latensi inferensi pada n=1.000 sampel uji menunjukkan Decision Tree memberikan keseimbangan terbaik (rerata 0,11 ms; persentil-95 0,18 ms; ukuran 1,06 KB), jauh di bawah ambang 100 ms yang direkomendasikan NIST SP 800-63B. Kontribusi utama studi ini adalah kerangka evaluasi multi-dimensi yang reproducible dengan kriteria efisiensi terkuantifikasi, bukan klaim akurasi yang trivial.
Classification of Students Major Preferences at SMKIT Ibnul Qayyim Using a Machine Learning Model Based on Students Knowledge, Skills, and Interests Rajie Al Qadri Anwar; Widyastuti Andriyani
PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic Vol. 14 No. 1 (2026): March 2026
Publisher : LPPM Universitas Islam 45 Bekasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33558/piksel.v14i1.12025

Abstract

This study aims to uncover the dynamics and meanings underlying the major preference process at SMK IT Ibnul Qayyim through the integration of a qualitative approach and the use of machine learning models based on student knowledge, skills, and interests. Major selection is a critical issue in vocational education, as mismatches often occur between student interests and the chosen majors, which can affect learning motivation and job readiness. This study adopts a qualitative case study approach involving ten participants, consisting of guidance and counseling teachers, homeroom teachers, and Grade IX–X students. Data were collected through semi-structured interviews, participatory observation, and document analysis. The data were analyzed using the interactive model of Miles and Huberman, including data reduction, data display, and conclusion drawing. The results reveal three main themes: (1) major determination is still largely influenced by academic achievement rather than skill potential and intrinsic interests; (2) students perceive machine learning-based prediction systems as objective decision-support tools, while emphasizing the importance of teacher involvement in interpreting the results; and (3) the integration of predictive technology with a humanistic approach is more effective in assisting students in determining majors that align with their personal profiles. This analysis aims to evaluate and predict major preferences of vocational high school students in the Software Engineering (Rekayasa Perangkat Lunak/RPL) program based on their academic achievement at the junior secondary school level. The data include scores from core subjects such as Computer Studies, Mathematics, English, Indonesian Language, Arts and Culture, Civic Education, and Social Studies. Two main analytical approaches are employed: Logistic Regression and Random Forest. These methods are selected because each offers distinct strengths in addressing the research objectives, not only in predicting major preferences but also in providing interpretability regarding the factors that influence student decision-making.
Co-Authors Akhmad Dahlan Andre Argisitawan Anwarudin Anwarudin Arif Setiadi, Rizki Arma Fauzi Asyahri Hadi Nasyuha B.T. Sutrisno Bagas Triaji Bambang P.D.P Bambang Purnomosidi Dwi Putranto Bradika Almandin Wisesa Brahmana, Ivanna Beru Brian Duen Rakly Cucut Hariz Pratomo D P, Bambang Purnomosidi Danny Kriestanto, Danny Dian Tri Wiyanti Dommy Kristomo Domy Kristomo, Domy Duen Rakly, Brian Dwi Wibowo Eny Retna Ambarwati Faizal Makhrus Faizal Makhrus Femi Dwi Astuti Femi Dwi Astuti Fika Pratiwi Firman Noor Hasan Hamdani Hamdani Hendra Hengki Hengki Heri Muhrial Herwantono, Herwantono Hizkia Hendra Rianingsih Istichomah Istichomah Ivónia Fátima Ruas da Silva Kuindra Iriyanta Laksono, Triyan Agung Miftahul Huda MILASARI, LISA ASTRIA Muhammad Ali Sofian Murgi Handari Nenen Isnaeni Nugroho, Daniel C.A. Nugroho, Muhammad Agung Nurohman, Muhamad P.D.P., Bambang Pangestika , Elza Qorina Pereira, Elisabet da Conceição Prisilia Talakua Pujianto Pujianto Purnomosidi D.P, Bambang Purnomosidi Dwi Putranto, Bambang Purnomosidi, Bambang Putra, Fadhlih Girindra Rajie Al Qadri Anwar Rakly, Brian Duen Reni Tri Lestari Retantyo Wardoyo Riadinata Riadinata Rifky Lana Rahardian Rikie Kartadie Robertus Saptoto Roh Bintang Jaya, Mabrur Ruas da silva, Ivonia Fatima Said, Famidin Saputra, Andika Jodhi Saryanto Saryanto Sipayung, Hotma Sadariahta Siti Khomsah, Siti Sri Redjeki Suningrat, Nining Suryanto Suryanto Taufik Ismail Totok Suprawoto Tri Andi, Tri Wibowo, Gunturari Wijayanti, Agnes Erida Wiwi Widayani, Wiwi Yohanni Syahra Yuli Astuti