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All Journal JURNAL EKONOMI DAN KEBIJAKAN PEMBANGUNAN Jurnal Manajemen dan Organisasi MANAJEMEN IKM: Jurnal Manajemen Pengembangan Industri Kecil Menengah FORUM STATISTIKA DAN KOMPUTASI Forum Pasca Sarjana Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Agro Ekonomi Analisis Kebijakan Pertanian Indonesian Journal of Business and Entrepreneurship (IJBE) Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Jurnal Ilmu Keluarga dan Konsumen Informatika Pertanian BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) IJID (International Journal on Informatics for Development) Jambura Journal of Mathematics Jurnal Kesehatan Ibu dan Anak Jurnal Matematika UNAND Indonesian Journal of Electrical Engineering and Computer Science Jurnal Agro Ekonomi Analisis Kebijakan Pertanian International Journal of Advances in Data and Information Systems ARRUS Journal of Mathematics and Applied Science Makara Journal of Technology Prosiding Seminar Nasional Official Statistics Scientific Journal of Informatics Indonesian Journal of Jamu Indonesian Journal of Statistics and Its Applications
Hari Wijayanto
Departemen Statistik, FMIPA Institut Pertanian Bogor
Agriculture, Biological Sciences & Forestry Humanities 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 Health Professions Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Prediksi Senyawa Aktif Pada Tanaman Obat Berdasarkan Kemiripan Struktur Kimiawi untuk Penyakit Diabetes Tipe II Bakri, Rizal; Wijayanto, Hari; Afendi, Farit Mochamad
Jurnal Jamu Indonesia Vol. 1 No. 3 (2016): Jurnal Jamu Indonesia
Publisher : Tropical Biopharmaca Research Center, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/jji.v1i3.18

Abstract

Diabetes melitus merupakan penyakit metabolik yang dicirikan oleh tingginya kadar glukosa dalam darah. Di Indonesia jumlah penderita diabetes menempati urutan keempat di dunia setelah Amerika Serikat, India, dan Cina dengan jumlah penderita mencapai lebih dari 12 juta jiwa. Salah satu upaya yang dilakukan untuk mengatasi diabetes adalah mengkonsumsi obat herbal berupa jamu sebagai alternatif obat sintetik. Pusat Studi Biofarmaka Bogor sedang mengembangkan ramuan jamu untuk penyakit Diabetes Melitus Tipe II yang terdiri dari empat tanaman obat yaitu pare (Momordica charantia), sembung (Blumea balsamifera), bratawali (Tinospora crispa), dan jahe (Zingiber officinale). Kandungan senyawa keempat tanaman diduga memiliki aktivitas biologis yang mirip dengan senyawa sintetik. Pada prinsipnya, diasumsikan bahwa senyawa yang struktur kimiawinya mirip memiliki sifat biologis yang mirip. Kemiripan senyawa diukur menggunakan koefisien Modifikasi Tanimoto dengan sidik jari molekuler KR. Hasil penelitian menunjukkan bahwa tanaman Bratawali merupakan tanaman utama pada ramuan jamu untuk penyakit diabetes berdasarkan jumlah kandungan senyawa yang dominan mirip dengan senyawa sintetik yaitu senyawa N-trans-feruloyltyramine (B015) dan N-formylanonaine (B018). Selanjutnya, Senyawa-senyawa yang memiliki nilai kemiripan tinggi dengan senyawa sintetik diperoleh pula pada senyawa karaviloside I (P195) dari tanaman pare, senyawa xanthoxylin (S002) dari tanaman sembung, senyawa borneol (J207) dan (-)- isoborneol (J226) dari tanaman Jahe.
Komparasi Sistem Remunerasi Pada Tiga Perguruan Tinggi Negeri Badan Hukum (PTNbh) Astridina, Astridina; Maarif, M. Syamsul; Wijayanto, Hari
Jurnal Manajemen dan Organisasi Vol. 8 No. 3 (2017): Jurnal Manajemen dan Organisasi
Publisher : IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (547.977 KB) | DOI: 10.29244/jmo.v8i3.22448

Abstract

 ABSTRACTThe research objective is to analyze the suitability of the design of the remuneration system in three PTNbh with preparation stages and principles of remuneration and evaluate the system of socialization and information systems used in the application of the remuneration of the three PTNbh. This research was conducted in three PTNbh located in Jakarta (PTNbh X), West Java (PTNbh Y) and East Java (PTNbh Z) using primary data obtained from in-depth interviews and secondary data derived from the literature, previous research, laws and regulations, government regulations and decrees that are relevant to the implementation of the remuneration of PTNbh. This study used a descriptive approach qualitative analysis benchmarking method. In the preparation of the remuneration system, which first assigned PTNbh not follow the stages of preparation with good remuneration, whereas previously PTNbh derived from State BLU more likely to obey the principle and the preparation of their remuneration has been prepared in detail based on the principles of remuneration and government regulations.ABSTRAKPenelitian ini bertujuan untuk menganalisis kesesuaian rancangan sistem remunerasi di tiga PTNbh dengan tahapan dan prinsip penyusunan remunerasi dan mengevaluasi sistem sosialisasi serta sistem informasi yang digunakan dalam penerapan remunerasi pada tiga PTNbh.  Penelitian ini dilakukan di tiga PTNbh yang berada di DKI Jakarta (PTNbh X), Jawa Barat (PTNbh Y) dan Jawa Timur (PTNbh Z) dengan menggunakan data primer yang diperoleh dari wawancara mendalam serta data sekunder yang berasal dari studi pustaka, penelitian terdahulu,  peraturan pemerintah yang berlaku dan surat keputusan yang relevan dengan penerapan remunerasi pada PTNbh. Penelitian ini menggunakan pendekatan deskriptif kualitatif dengan metode analisis patok duga. Dalam penyusunan sistem remunerasi, PTNbh yang lebih dulu ditetapkan belum mengikuti tahapan penyusunan remunerasi dengan baik, sedangkan PTNbh yang sebelumnya berasal dari PTN BLU cenderung lebih taat azas dan penyusunan remunerasinya sudah disusun dengan detil berdasarkan prinsip-prinsip remunerasi dan peraturan pemerintah.
Sentiment Analysis of Tokopedia Customer Reviews Using BiLSTM and IndoBERT with Comparative Analysis of Preprocessing and Labeling Methods Anadra, Rahmi; Wijayanto, Hari; Sadik, Kusman
International Journal of Advances in Data and Information Systems Vol. 6 No. 3 (2025): December 2025 - International Journal of Advances in Data and Information Syste
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/ijadis.v6i3.1458

Abstract

This study addresses key challenges in Indonesian sentiment analysis related to preprocessing, labeling strategies, and class imbalance. It compares the performance of BiLSTM and IndoBERT using user reviews collected from Tokopedia. The dataset was manually and automatically labeled, then processed under three preprocessing schemes. Both models were trained with tuned hyperparameters and imbalance-handling techniques and evaluated through twenty rounds of stratified five-fold cross-validation. Performance was assessed using balanced accuracy and F1-score. IndoBERT achieved the highest results, with balanced accuracy up to 0.85 and F1-scores up to 0.83, while BiLSTM reached balanced accuracy up to 0.78 and F1-scores up to 0.76. Applying class weight and focal loss improved model performance by approximately 2% to 11% over the baseline. BiLSTM demonstrated greater training efficiency, requiring only 1 to 2.5 minutes per epoch, compared with IndoBERT’s 2.6 to 3.6 minutes. Although manual labeling remained superior in capturing contextual nuance and emotional cues, GPT-based labeling showed strong agreement with the human annotations. A four-way ANOVA revealed that all main factors and several interactions significantly influenced classification outcomes. Overall, BiLSTM provides faster training efficiency, whereas IndoBERT delivers higher predictive accuracy.
Meta-stacking models for electricity load forecasting in West Java Denanda Aufadlan Tsaqif; Bagus Sartono; Hari Wijayanto
Indonesian Journal of Electrical Engineering and Computer Science Vol 42, No 2: May 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v42.i2.pp442-453

Abstract

Indonesia’s electricity demand continues to increase due to population growth, urbanization, and industrial expansion, therefore making accurate load forecasting is essential to maintain supply-demand balance. However, electrical load demand in West Java has a complex pattern (seasonality, nonlinear behavior, weather variability, and holiday effects), which motivates the use of a meta-stacking approach to effectively capture such complexity. Previous research shows that meta-stacking outperforms individual models, but it fails to capture sudden changes and its performance consistency remains unclear. Therefore, this study proposes a meta-stacking framework for daily electricity load forecasting in West Java (2006-2023) that includes weather and holiday variables by combining CNN-BiLSTM, CNN-BiGRU, and Windowed-XGBoost forecasts through linear regression and evaluates its performance across five data-splitting scenarios and nine forecast horizons, which represents the main novelty in this research. Meta stacking shows strong generalization across scenarios and strong long-term forecasting performance across horizons, while consistently providing a balanced trade-off between MAPE and trend accuracy, where the model trained on the longest historical dataset achieves the best performance with 1.89% MAPE and 86% trend accuracy. The proposed approach successfully captures seasonal and holiday-related load patterns, indicating its potential to support PLN in improving demand planning and operational decision making.
LDA Topic Modeling Analysis of Public Discourse on Indonesia’s Free Nutritious Meals Program (MBG) Cici Suhaeni; Laily Nissa Atul Mualifah; Hari Wijayanto
IJID (International Journal on Informatics for Development) Vol. 14 No. 1 (2025): IJID June
Publisher : Faculty of Science and Technology, Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/ijid.2025.5211

Abstract

This study investigates public discourse on Indonesia's Free Nutritious Meals (Makan Bergizi Gratis/MBG) program through Latent Dirichlet Allocation (LDA) topic modeling of YouTube comments. Filling a research gap on online public opinion regarding the MBG policy, this study identifies dominant themes and discursive patterns in public perception. A three-topic model, validated through coherence score evaluation and pyLDAvis visualization, reveals key topics: concerns over food prices and distribution, perceived benefits for children and society, and emotionally and politically driven reactions. The findings provide valuable insights into public opinion, while also highlighting challenges in processing Indonesian-language text, such as informal language and noisy data. This study contributes to understanding public perceptions of social policies in digital environments and recommends future research directions, including improved text preprocessing and alternative topic modeling approaches. By shedding light on online public discourse, this research informs policymakers and stakeholders about the effectiveness and potential areas for improvement in the MBG program.
Sentiment Classification on the 2024 Indonesian Presidential Candidate Dataset Using Deep Learning Approaches Cici Suhaeni; Hari Wijayanto; Anang Kurnia
Indonesian Journal of Statistics and Applications Vol 8 No 2 (2024)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v8i2p83-94

Abstract

This study aims to compare the performance of three deep learning models (LSTM, BiLSTM, and GRU) in the task of sentiment classification for the 2024 Indonesian Presidential Candidate dataset, focusing specifically on the case of Prabowo Subianto. The dataset comprises social media X posts sourced from kaggle, and the analysis investigates the effectiveness of different variants of recurrent neural network architectures in identifying public sentiment. The models were evaluated on accuracy and F1 score. The results demonstrate that BiLSTM outperformed both LSTM and GRU models in all metrics, achieving a testing accuracy of 80.70% and an F1 score of 86.86%, compared to LSTM and GRU which both achieved a testing accuracy of 72.56% and an F1 score of approximately 84%. The higher performance of BiLSTM is attributed to its ability to capture bidirectional context within the text, thereby understanding complex sentiment patterns more effectively. LSTM and GRU models displayed similar performance, therefore BiLSTM is the best model for this dataset. These results indicate that BiLSTM is especially well-suited for analyzing public sentiment towards political figures like Prabowo Subianto, offering significant insights into public discussions surrounding the 2024 Indonesian Presidential Election. This study recommends exploring transformer-based models like BERT or GPT variants to enhance sentiment classification accuracy in this domain.
PATIENT EMPOWERMENT INDEX OF DIABETES MELLITUS PATIENTS Agus Heru Darjono; Ujang Sumarwan; Lilik Noor Yuliati; Hari Wijayanto
Jurnal Ilmu Keluarga dan Konsumen Vol. 12 No. 3 (2019): JURNAL ILMU KELUARGA DAN KONSUMEN
Publisher : Faculty of Social Science and Human Ecology, IPB University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (299.447 KB) | DOI: 10.24156/jikk.2019.12.3.260

Abstract

The measurement of patient empowerment is important in the health care of chronic diseases, especially diabetes mellitus. The purpose of the study was to develop the patient empowerment index (‘IKP’/Indeks Keberdayaan Pasien) and its dimensions (patient knowledge, patient control, and patient participation) in patients with diabetes mellitus. The research utilized factor analysis in developing patient empowerment index for data analysis. Purposive sampling has been conductedwith 330respondents of diabetes mellitus patients from 26 hospitals in Jabotabek (Jakarta, Bogor, Tangerang, and Bekasi). The variables measured using the Likert scale with a scale of 1 to 5, in which 1 indicates the level of strongly disagree and 5 indicates the level of strongly agree. The collected data were analysed with factor analysis.The results showed that the patient empowerment index consisted of 25.84 percent of knowledge dimensions, 33.44 percent of control dimensions and40.76 percent of participation dimensions. The total score of patient empowerment index value is 68.84 that is in the critical category, which means that consumers have control for the management of their disease conditions in their daily lives. The managerial implication based on the result was the emerging issues of the government to develop an empowerment index for each province in Indonesia that can be used as a benchmark and key performance indicator (KPI) to measure the governance of health programs so the patient empowerment can be increased.
Co-Authors . Aunuddin . Barizi . Gunawan Aan Kardiana Afnan, Irsyifa Mayzela Agus Heru Darjono Agus Mohamad Soleh Aji Hamim Wigena Akhmad Fauzi Aldi Cahyanugroho Anadra, Rahmi Anang Kurnia Andres Purmalino Anggraini Sukmawati Aqmar, Nurzatil Arief Hendarto Arif Handoyo Marsuhandi Aruddy Aruddy ASEP SAEFUDDIN Astridina, Astridina Aunuddin Aunuddin Baba Barus Bagus Sartono Bambang Hendro Trisasongko Barizi . Basita G. Sugihen Bertho Tantular Boedi Tjahjono Budi Susetyo Cici Suhaeni Cici Suhaeni Cut Zaraswati DAHRUL SYAH Dede Dirgahayu Domiri Dedi Budiman Hakim Denanda Aufadlan Tsaqif Dyah R Panuju Dyah R. Panuju Dyah R. Panuju Edi Abdurrachman Eko S. Pribadi Erfiani Erfiani Erliza Noor Fachry Abda El Rahman Farit Mochamad Afendi Farly Shabahul Khairi fatimah Fatimah Fitria Hasanah Fitrianto, Anwar H S, Rahmat Hikmah, Zetil I K Marla Lusda I Made Sumertajaya Ilma, Meisyatul Ina Widayanty Indahwati Irzaman, Irzaman Istiqlaliyah Muflikhati Jajah K. Wagiono Jayawarsa, A.A. Ketut Khairil Anwar Notodiputro Kurnia Suci Indraningsih Kusman Sadik La Ode Abdul Rahman Laily Nissa Atul Mualifah Leny Maryesa Lilik Noor Yuliati Luvy Mayanda M. Syamsul Maarif Mahmud A. Raimadoya Mahmud A. Raimadoya Mualifah, Laily Nissa Atul Muhammad Nur Aidi Musa Hubeis Nunung Nurjanah Nurrahman, Fathu Panca Wiputra Pang S. Asngari Pannu, Abdullah Prabowo Tjitrpranoto Riana Riskinandini Rizal Bakri Rizky Nurkhaerani Rysda Rysda Sachnaz Desta Oktarina Siti Hafsah Ujang Sumarwan Utami Dyah Syafitri Yarah, Helena Ramadhini Yenni Angraini Yuni Suci Kurniawati Zaenal, Mohamad Solehudin