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All Journal RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Sistem Cerdas Buletin Sistem Informasi dan Teknologi Islam Jurnal Masyarakat Madani Indonesia Jurnal Teknoinfo
Angga Kurniawan
Universitas Telkom
Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering Library & Information Science Public Health Social Sciences

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Peningkatan Literasi dan Kepedulian Lingkungan Anak melalui Program Penukaran Sampah dengan Buku dan Mainan Edukasi di Desa Muntang Dasril Aldo; Muhammad Raafi'u Firmansyah; Evia Zunita Dwi Pratiwi; Yohani Setiya Rafika Nur; Angga Kurniawan; Affriza Brilyan Relo Pambudi Agus Putra
Jurnal Masyarakat Madani Indonesia Vol. 4 No. 4 (2025): November
Publisher : Alesha Media Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59025/e1jfeb45

Abstract

Rendahnya literasi dan kepedulian lingkungan pada anak-anak di wilayah pedesaan masih menjadi permasalahan utama, termasuk di Desa Muntang, Kabupaten Purbalingga. Kurangnya akses terhadap bahan ajar interaktif dan minimnya kebiasaan memilah sampah menyebabkan anak-anak belum memiliki perilaku ramah lingkungan yang konsisten. Kegiatan pengabdian kepada masyarakat ini bertujuan meningkatkan literasi dan kesadaran lingkungan anak usia 4–6 tahun melalui program penukaran sampah dengan buku dan mainan edukatif. Program dilaksanakan oleh dosen dan mahasiswa Universitas Telkom Purwokerto bekerja sama dengan Komunitas Limbah Pustaka menggunakan pendekatan pembelajaran berbasis pengalaman (learning by playing) dan sistem penghargaan (reward-based learning). Sebanyak 35 anak mengikuti kegiatan yang mencakup sesi dongeng interaktif, penukaran sampah anorganik seperti plastik dan kertas dengan buku bergambar, serta permainan edukatif bertema lingkungan. Hasil kegiatan menunjukkan 88,6% anak fokus mendengarkan cerita, 85,7% mampu memilah sampah dengan benar, dan 94,3% aktif dalam permainan edukatif. Anak-anak menunjukkan peningkatan antusiasme dan pemahaman terhadap kebersihan lingkungan. Program ini terbukti efektif menumbuhkan perilaku pro-lingkungan sejak dini dan berkelanjutan melalui pembentukan Pojok Literasi Hijau di Desa Muntang.
Analysis of Diabetes Classification Performance Improvement Using Ensemble Bagging and K-Fold Mawardi Kudin; Abd Salam At Taqwa; Angga Kurniawan; Chairi Nur Insani
Jurnal Sistem Cerdas Vol. 9 No. 1 (2026)
Publisher : APIC

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37396/jsc.v9i1.630

Abstract

Diabetes mellitus represents a long-term metabolic disorder whose global incidence continues to rise, making precise early identification essential to minimize severe complications. Machine learning techniques have been extensively utilized for diabetes classification; however, single-model approaches often suffer from performance constraints, such as susceptibility to overfitting and high variability in prediction outcomes. To address these challenges, this research introduces a bagging-based ensemble learning strategy integrated with K-Fold Cross Validation to enhance both predictive accuracy and model robustness. The study employs the Pima Indians Diabetes Dataset, which contains 768 patient records described by eight clinical features and one outcome variable. Eight classification methods—Logistic Regression, K-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, Naïve Bayes, Gradient Boosting, and XGBoost—were assessed individually and within the proposed ensemble framework. Model effectiveness was measured using accuracy, precision, recall, and F1-score derived from the confusion matrix. The findings indicate that the ensemble bagging approach generally strengthens model stability and yields improvements in accuracy and precision across most algorithms. Notably, K-Nearest Neighbors and XGBoost demonstrated the most stable gains following ensemble integration. Nevertheless, enhancements in precision were frequently associated with a reduction in recall, reflecting a trade-off in identifying positive cases. In summary, the integration of bagging and K-Fold Cross Validation provides a more resilient and dependable classification model, offering strong potential for supporting clinical decision-making in early diabetes detection.
Gradient Boosting Teroptimasi untuk Klasifikasi Diabetes dengan Analisis Eksplanabilitas Menggunakan SHAP dan LIME Angga Kurniawan; Nisrina Hanifa Setiono; Afin Muhammad Nurtsani; Andi Hisyam Helmi Faalih Fakhruddin; Muhammad Naufal Farabbia; Nadia Syahda Fitriani
Buletin Sistem Informasi dan Teknologi Islam (BUSITI) Vol 7, No 2 (2026)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v7i2.3413

Abstract

Penelitian ini mengusulkan kerangka klasifikasi diabetes yang mengintegrasikan tiga algoritma gradient boosting, yaitu XGBoost, LightGBM, dan CatBoost, yang dioptimasi secara otomatis menggunakan Optuna berbasis optimasi Bayesian dengan 50 percobaan dan cross validation 5-folds. Dataset yang digunakan adalah Pima Indians Diabetes Dataset dengan 768 sampel dan 8 fitur klinis. Preprocessing dilakukan menggunakan beberapa metode meliputi penggantian nilai nol sebagai nilai hilang, imputasi median dari data pelatihan, serta penambahan fitur indikator Insulin_missing. Ketidakseimbangan kelas ditangani melalui parameter scale_pos_weight yang dioptimasi bersama hiperparameter model dalam satu ruang pencarian. Evaluasi model menggunakan metrik AUC-ROC, akurasi, F1-score, recall, dan presisi dengan ambang batas klasifikasi 0,6. Analisis eksplanabilitas dilakukan menggunakan SHAP pada level global dan lokal serta LIME pada level instans untuk meningkatkan transparansi model. Hasil optimasi menunjukkan CatBoost mencapai AUC-ROC cross validation tertinggi sebesar 0.8471, diikuti LightGBM sebesar 0.8409 dan XGBoost sebesar 0.8406.  Pada data uji, XGBoost mencapai AUC-ROC 0,8246, akurasi 0,753, F1-Score 0,683, dan recall kelas diabetes 0,759, sedangkan CatBoost mencapai recall tertinggi 0,888 dengan F1-Score 0,690. Analisis SHAP secara konsisten mengidentifikasi Glucose, BMI, Age, dan DiabetesPedigreeFunction sebagai empat prediktor paling berpengaruh di ketiga model, selaras dengan pengetahuan klinis mengenai faktor risiko utama diabetes tipe 2.
Transformasi Pengetahuan dan Perilaku Pencegahan Demam Berdarah pada Anak melalui Inovasi Edukasi Digital “DBD Defender” Aminatus Sa'adah; Dasril Aldo; Angga Kurniawan; Gusnita Linda; Elisa Kusumaningsih; Muhammad Nafal Fiqrian; M. Hanif Al Faiz
Jurnal Masyarakat Madani Indonesia Vol. 5 No. 2 (2026): Mei
Publisher : Alesha Media Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59025/b0kw4344

Abstract

Demam Berdarah Dengue (DBD) masih menjadi permasalahan kesehatan yang sering terjadi di lingkungan masyarakat, khususnya pada anak-anak yang memiliki keterbatasan dalam memahami dan menerapkan perilaku pencegahan. Rendahnya pengetahuan dan kesadaran anak terhadap kebersihan lingkungan menjadi faktor utama meningkatnya risiko penularan penyakit ini. Kegiatan ini bertujuan untuk meningkatkan pengetahuan dan perilaku pencegahan Demam Berdarah Dengue pada anak melalui inovasi edukasi digital “DBD Defender”. Metode yang digunakan adalah pendekatan berbasis partisipatif dengan tahapan sosialisasi, pengenalan media, pelatihan penggunaan aplikasi, serta evaluasi melalui pre-test dan post-test serta observasi perilaku. Hasil kegiatan menunjukkan adanya peningkatan rata-rata pengetahuan peserta dari 41,2% pada pre-test menjadi 90,0% pada post-test, dengan peningkatan sebesar 48,8%. Peningkatan tersebut mencakup pemahaman anak terhadap penyebab, penularan, gejala, dan langkah pencegahan penyakit, serta perubahan awal pada perilaku hidup bersih dan sehat. Selain itu, peserta menunjukkan tingkat keterlibatan dan antusiasme yang tinggi selama penggunaan media edukasi digital. Dengan demikian, inovasi “DBD Defender” efektif sebagai media edukasi digital dalam mendukung upaya pencegahan Demam Berdarah Dengue pada anak secara berkelanjutan.
EVALUATION OF MACHINE LEARNING AND DEEP LEARNING ALGORITHMS WITH FEATURE SCALING AND K-FOLD CROSS-VALIDATION FOR DIABETES CLASSIFICATION ANGGA KURNIAWAN; MAWARDI KUDIN; ABDUL SALAM AT-TAQWA
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7005

Abstract

Diabetes is a growing global health challenge that requires advanced approaches for early detection and prevention. Previous research has often been limited to evaluation using a single data split, which can potentially yield unreliable model performance estimates. This study addresses that limitation by conducting a comprehensive and rigorous evaluation of eight machine learning algorithms—including classical models, ensemble methods, and Multi-Layer Perceptron (MLP)—using the Pima Indians Diabetes dataset. The applied methodology includes data preprocessing, systematic hyperparameter optimization, and, most importantly, robust performance validation through Multi K-Fold Cross-Validation (K=5,10,15,20). Initial results showed perfect accuracy (100%) for the K-Nearest Neighbors (KNN) model; however, this finding was proven to be an artifact of a fortunate data split (lucky split) after undergoing cross-validation procedures. The more reliable validation results instead revealed the exceptional superiority of the Multi-Layer Perceptron (MLP) model, which achieved 94.96% accuracy with high stability (standard deviation 0.0356) in 20-fold cross-validation. Meanwhile, classical models such as Logistic Regression and Support Vector Machine (SVM) demonstrated high robustness and consistency. These findings significantly contribute to the field of health informatics by emphasizing the importance of rigorous validation methodology and identifying MLP as a highly strong predictive model candidate for diabetes detection. For practical application, this study recommends MLP along with stable classical models as the foundation for developing reliable clinical decision support systems, with the note that further external validation testing is necessary.
Meningkatkan Literasi Digital Siswa SMA/SMK melalui Kurikulum Koding Berbasis Project-Based Learning di Sulawesi Selatan Angga Kurniawan; Mawardi Kudin; Abdul Salam At-Taqwa; Nisrina Hanifa Setiono
Jurnal Masyarakat Madani Indonesia Vol. 5 No. 2 (2026): Mei
Publisher : Alesha Media Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59025/q3fcw725

Abstract

Program pengabdian ini bertujuan meningkatkan literasi digital siswa SMA/SMK di Sulawesi Selatan melalui kurikulum koding berbasis Project-Based Learning (PjBL) selama 16 sesi. Mitra menghadapi tiga masalah utama: belum ada pembelajaran koding sistematis, rendahnya kemampuan computational thinking, dan kesiapan guru terbatas dalam PjBL. Program dilaksanakan daring via Zoom, melibatkan 86 siswa dari tiga sekolah (SMAN 3 Bone, SMAN 12 Bone, SMAN 9 Bulukumba). Metode PjBL memandu peserta membuat aplikasi (kalkulator, kasir) berbasis Python/web. Hasil evaluasi menunjukkan peningkatan rata-rata kognitif sebesar 40.2 poin (84%) dari pre-test ke post-test, dengan 82.5% peserta menghasilkan proyek akhir berkategori "Memuaskan" hingga "Sangat Memuaskan". Rata-rata kehadiran 85,7%. Program ini terbukti efektif meningkatkan literasi digital dan keterampilan koding aplikatif, serta menghasilkan model replikasi untuk pendidikan literasi digital terukur di daerah lain.
Building a Digital Lexical Resource for Banyumasan Javanese: A Low-Resource Language Approach Nisrina Hanifa Setiono; Angga Kurniawan; Viga Laksa Hardjanto
Jurnal Teknoinfo Vol. 20 No. 2 (2026): Period July 2026
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/teknoinfo.v20i2.1705

Abstract

Banyumasan Javanese, widely recognized through the Ngapak dialect, remains culturally significant but is still underrepresented in reusable computational resources. This study develops a digital Banyumasan-Indonesian lexical corpus and frames it as a reusable research artifact rather than a static appendix. The corpus was constructed from a Banyumasan-Indonesian dictionary, normalized into a structured bilingual dataset, and packaged as an installable Python resource so that it can be used directly in computational experiments. The implemented resource supports dataset loading, Banyumasan lookup, Indonesian lookup, simple translation, structured translation analysis, batch translation, and corpus statistics. The resulting corpus contains 2,000 lexical pairs, 1,996 unique Banyumasan forms, 1,444 unique Indonesian equivalents, and 4 duplicated Banyumasan headwords that preserve lexical ambiguity from the source material. To demonstrate practical utility, the study includes a 100-sentence implementation example in which Banyumasan text is translated with the published banyumasan-corpus package and evaluated against Indonesian ground truth using the Indonesian-focused embedding model LazarusNLP/all-indo-e5-small-v4. The average semantic similarity rises from 0.4833 for direct Banyumasan-versus-ground-truth comparison to 0.6427 after translation, producing an absolute gain of 0.1594 and a relative improvement of approximately 33.0% over the baseline. These findings indicate that a structured lexical corpus, when distributed in a directly reusable computational form, can strengthen both resource accessibility and small-scale downstream experimentation for a low-resource regional language.
Co-Authors Abd Salam At Taqwa ABDUL SALAM AT-TAQWA Affriza Brilyan Relo Pambudi Agus Putra Afin Muhammad Nurtsani Aminatus Sa'adah Andi Hisyam Helmi Faalih Fakhruddin Chairi Nur Insani Dasril Aldo Dasril Aldo Elisa Kusumaningsih Evia Zunita Dwi Pratiwi Gusnita Linda M. Hanif Al Faiz Mawardi Kudin Muhammad Nafal Fiqrian Muhammad Naufal Farabbia Muhammad Raafi'u Firmansyah Nadia Syahda Fitriani Nisrina Hanifa Setiono Nisrina Hanifa Setiono Nisrina Hanifa Setiono Viga Laksa Hardjanto Yohani Setiya Rafika Nur