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All Journal International Journal of Electrical and Computer Engineering Pedagogi: Jurnal Ilmu Pendidikan Jurnal Teknik Sipil dan Perencanaan Jurnal Agro ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Jurnal Teknik Sipil Sistemasi: Jurnal Sistem Informasi Rancang Bangun Jurnal Media Pertanian Jurnal Teknologi Pertanian International Journal of Supply Chain Management JURNAL PERTANIAN Jurnal Agroecotania : Publikasi Nasional Ilmu Budidaya Pertanian Journals of Ners Community Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal JOURNAL OF SCIENCE AND SOCIAL RESEARCH JISIP: Jurnal Ilmu Sosial dan Pendidikan INTEK: Jurnal Penelitian TELKA - Telekomunikasi, Elektronika, Komputasi dan Kontrol YARSI Medical Journal Jurnal Aplikasi Akuntansi Jurnal Pengabdian Kepada Masyarakat (Mediteg) Valid Jurnal Ilmiah Buletin Ilmiah Sarjana Teknik Elektro PRIMA: Journal of Community Empowering and Services Abdi Masyarakat Abdimas Galuh: Jurnal Pengabdian Kepada Masyarakat Indonesian Journal of Computing, Engineering, and Design SELODANG MAYANG JAREE (Journal on Advanced Research in Electrical Engineering) Jurnal Teknik Informatika (JUTIF) Jurnal Teknisi Kontribusi: Jurnal Penelitian dan Pengabdian Kepada Masyarakat Jurnal Karya Abdi Masyarakat Jurnal Ilmiah Ilmu Terapan Universitas Jambi Media Bina Ilmiah Journal Industrial Engineering and Management (JUST-ME) Jurnal Fisioterapi dan Kesehatan Indonesia Fusion : Journal of Research in Engineering, Technology and Applied Sciences Student Journal Nutrition (SJ Nutrition) Jurnal Teknik AMATA
Irianto Irianto
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PENERAPAN MFEP DALAM MEREKOMENDASIKAN PENERIMA PINJAMAN MODAL USAHA KECIL MENENGAH DI BUMDES TANJUNG ASRI Irianto, Irianto; Amin, Muhammad; Maulana, Cecep
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol 8, No 2 (2025): May 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i2.3085

Abstract

Abstract: Determining loan recipients for Small and Medium Enterprises (SMEs) at BUMDes Tanjung Asri requires an objective method to ensure that assistance is provided to truly eligible parties. This study applies the Multi-Factor Evaluation Process (MFEP) method to provide more structured recommendations based on relevant criteria. The evaluation process includes factors such as business feasibility, loan repayment ability, business experience, and other financial aspects. Data were collected through interviews and document studies, then analyzed using the MFEP method to rank loan applicants. The results indicate that the implementation of MFEP enhances transparency and accuracy in decision-making compared to conventional methods. With an MFEP-based system, BUMDes Tanjung Asri can allocate funds more effectively, ensuring targeted financial support and promoting sustainable local economic growth. Keywords: MFEP, Recommendation, Business Loan, UKM, BUMDes Abstrak: Penentuan penerima pinjaman modal bagi Usaha Kecil Menengah (UKM) di BUMDes Tanjung Asri memerlukan metode yang objektif agar bantuan dapat diberikan kepada pihak yang benar-benar layak. Penelitian ini menerapkan metode Multi-Factor Evaluation Process (MFEP) untuk memberikan rekomendasi yang lebih terstruktur dan berbasis kriteria yang relevan. Faktor-faktor yang digunakan dalam proses evaluasi meliputi kelayakan usaha, kemampuan pengembalian pinjaman, pengalaman usaha, serta aspek finansial lainnya. Data dikumpulkan melalui wawancara dan studi dokumentasi, kemudian dianalisis menggunakan metode MFEP untuk menghasilkan peringkat calon penerima pinjaman. Hasil penelitian menunjukkan bahwa penerapan MFEP dapat meningkatkan transparansi dan akurasi dalam pengambilan keputusan dibandingkan metode konvensional. Dengan adanya sistem berbasis MFEP, BUMDes Tanjung Asri dapat mengalokasikan dana secara lebih tepat sasaran, sehingga mendukung pertumbuhan ekonomi lokal secara berkelanjutan. Kata kunci: MFEP, Rekomendasi, Pinjaman Modal, UKM, BUMDes
Classification of Obesity Using The Naïve Bayes Method and K-Nearest Neighbor Ari, Ilham Asy; Amin, Muhammad; Saputra, Andi; Irianto, Irianto; Manurung, Nuriadi
JURNAL TEKNISI Vol 6, No 1 (2026): February 2026
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/teknisi.v6i1.5529

Abstract

Abstract: Obesity is a major health problem that significantly impacts quality of life and can trigger various chronic diseases. Early detection of obesity levels is crucial for public health management, but traditional methods such as BMI often have limitations. Solution: This study proposes a data mining-based approach using feature engineering techniques to improve the accuracy of obesity classification. The purpose of this study is to classify obesity levels and compare the performance of Naïve Bayes and K-Nearest Neighbor (KNN) methods. This research method includes preprocessing stages, feature extraction using Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), feature selection using CFS and Chi-Square, classification with Naïve Bayes and KNN, and model evaluation using accuracy and confusion matrix on 2,111 data sets from Kaggle. The results of this study show that on the original data without LDA, KNN achieves a higher accuracy (88.41%) than Naïve Bayes (63.82%). However, after using LDA, the accuracy of Naïve Bayes increased sharply to 93.61%, surpassing KNN's 92.19%. The study concluded that KNN was more effective on raw data, while Naïve Bayes was more optimal when combined with LDA-based dimensionality reduction. Keyword:  classification_ naïve_bayes; data mining; k-nearest neighbor; obesity; LDA; PCA.  Abstract: Obesitas merupakan salah satu masalah kesehatan utama yang berdampak signifikan pada kualitas hidup dan dapat memicu berbagai penyakit kronis. Deteksi dini tingkat obesitas sangat krusial untuk manajemen kesehatan masyarakat, namun metode tradisional seperti BMI seringkali memiliki keterbatasan. Solusi: Penelitian ini mengusulkan pendekatan berbasis penambangan data (data mining) menggunakan teknik rekayasa fitur untuk meningkatkan akurasi klasifikasi tingkat obesitas. Tujuan penelitian ini adalah untuk mengklasifikasikan tingkat obesitas dan membandingkan kinerja metode Naïve Bayes dan K-Nearest Neighbor (KNN). Metode penelitian ini mencakup tahap prapemrosesan, ekstraksi fitur menggunakan Principal Component Analysis (PCA) dan Linear Discriminant Analysis (LDA), seleksi fitur menggunakan CFS dan Chi-Square, klasifikasi dengan Naïve Bayes dan KNN, serta evaluasi model menggunakan accuracy dan confusion matrix pada 2.111 data dari Kaggle. Hasil penelitian ini menunjukkan bahwa pada data asli tanpa LDA, KNN mencapai akurasi lebih tinggi (88,41%) dibandingkan Naïve Bayes (63,82%). Namun, setelah penggunaan LDA, akurasi Naïve Bayes meningkat tajam menjadi 93,61%, melampaui KNN yang mencapai 92,19%. Kesimpulan dari penelitian ini adalah KNN lebih efektif pada data mentah, sedangkan Naïve Bayes menjadi lebih optimal ketika dikombinasikan dengan reduksi dimensi berbasis LDA. Keywords: klasifikasi naïve bayes; k-nearest neighbor; obesitas; PCA; penambangan data; LDA
Peningkatan Produksi Sayuran dan Ikan secara Terpadu dalam Sistem Bioflok-Akuaponik di Kelurahan Tanjung Johor Kota Jambi Zulkarnain, Zulkarnain; Eliyanti, Eliyanti; Ichwan, Budiyati; Irianto, Irianto; Adriani, Adriani
PRIMA: Journal of Community Empowering and Services Vol 6, No 2 (2022): December
Publisher : Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/prima.v6i2.58144

Abstract

Increasing Production of Integrated-Vegetable and Fish in the Biofloc-Aquaponic System in Tanjung Johor Village, Jambi City. Tanjung Johor is a fish-producing center in Jambi City. However, the biofloc pond of Sekintang Dayo Farmers Group was minimally managed, so the fish yield and the farmer's income is low. The technology that was the potential to be applied was biofloc-aquaponics. Biofloc-aquaponics technology is a technology in agriculture which is integrated vegetable and fish cultivation. This technology was considered adaptive and effective in Tanjung Johor Village because this area was a populated residential area, limited land availability, prone to flooding, and the community still faced food sufficiency problems. The main principle of this technology was to conserve soil and water and increase farming efficiency through the use of nutrients from fish feed residues as a nutrient source for plants, so it is environmentally friendly. The objective of this community service was to overcome problems in the community, especially members of the Sekintang Dayo farmer group, through simple environmentally friendly technology, namely biofloc-aquaponics in integrated fish and vegetable cultivation. This activity used Participatory Rural Appraisal (PRA) method, where members of the Sekintang Dayo Fish Farmer Group interact with each other to identify the problems they face and find appropriate solutions. Group members actively followed, applied, and developed technology of biofloc-aquaponics cultivation. The program evaluation at the final stage of this activity showed an improvement in farmers' understanding and skills from 49.29% to 97.14%. The farmer group members are admitted to develop the knowledge gained and will design their own biofloc-aquaponic device by utilizing existing resources.
Comparative Analysis Of Machine Learning Algorithms For Dengue Fever Prediction Based On Clinical And Laboratory Features Sriyanto, Sriyanto; Aziz, RZ Abdul; Rahayu, Dewi Agushinta; Zuriati, Zuriati; Abdollah, Mohd Faizal; Irianto, Irianto
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 6 (2025): JUTIF Volume 6, Number 6, Desember 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.6.5309

Abstract

Dengue fever (DF) remains a global health problem requiring accurate early detection to prevent severe complications. This study applies machine learning (ML) algorithms to clinical and laboratory data for improving diagnostic accuracy. Six classifiers were compared: Decision Tree (DT), K-Nearest Neighbor (KNN), Logistic Regression (LR), Naïve Bayes (NB), Neural Network (NN), and Support Vector Machine (SVM). The dataset consists of 1,003 patient records with nine feature columns, of which 989 were used after preprocessing. Class distribution was imbalanced, with 67.6% positive and 32.4% negative cases. Model performance was evaluated using 10-fold cross-validation based on accuracy, precision, recall, F1-score, confusion matrix, and ROC curve analysis. The results indicate that DT achieved the highest performance with 99.4% accuracy, 99.4% precision, 99.7% recall, and 99.6% F1-score, slightly outperforming NN. KNN, LR, and SVM produced comparable results, while NB showed substantially lower accuracy (44.3%) and limited discriminatory power. ROC analysis confirmed these findings, with DT, NN, SVM, and LR achieving AUC values between 0.992 and 0.999, whereas NB performed poorly. These findings highlight the strong potential of ML algorithms, particularly DT, to support medical decision systems, strengthen informatics-based decision support applications, and enhance the accuracy and speed of dengue diagnosis in clinical practice.
Co-Authors Abdi, L. Khairul Abdollah, Mohd Faizal Abram, Voneka Adiyasa, I Nyoman ADRIANI ADRIANI afrisawati, Afrisawati Agusalim, Imam Dui Ahmad Firyal Adila AHMAD RIDUAN Ahmad Yani Alfian Ma’arif Ali, Esam Abu Baker Amanah, Ayu Amin, Muhammad Amin Amrul, Rusli Andi Saputra Anis Tatik Mariyani Anis Tatik Maryani Apriyanto, Mulono Ari, Ilham Asy Arman Jaya Arman Syahputra, Arman Arsana, I Nengah Asih, Hayati Mukti Asniwita Asniwita Ayu Wulandari Aziz, RZ. Abdul Bairizki, Ahmad Berthin Samuati Banga Budiyati Ichwan Bukri, Bukri Chandradewi , AASP Chandradewi Dedy Antony Dewi Agushinta R. Djauhari, Maman Abdurachman Eliyanti Eliyanti Endro Wahjono, Endro Epyk Sunarno Erlangga, Dina Surya Ermadani Ermadani Faizathul Octavia, Yusi Farhan, M Farhan Anantri Pramudya Farid Dwi Murdianto Febri Dristyan Ferdiansyah, Indra Firman, La Ode Muhammad Firmansyah Firmansyah FIRMANSYAH, ACHMAD DICKY Fitria Fitria Fuad Nurdiansyah Godfried San Ferre Hasana, Dina Nur Helmida, Baiq Ertin Hidayaturrahmi Hutapea, Melina Indhana Sudiharto Intan Sari Jamil Alsayaydeh, Jamil Abedalrahim Joko Triyatno, Joko Juliandari, Handayani Khotmi, Herawati Kisnawati, Baiq Lalu Khairul Abdi Lantaka, Okrisye Lapian, Franky E P Lapian, Franky E.P Lapian, Franky E.P. Lesirolo, Tresia Lesirolo, Tresia F Limbong, Ayulita Lotoshynska, Nataliia Lucky Pradigta Setiya Raharja Mabui, Didik S S Mabui, Didik S.S Made Darawati Mahyudin, Dimas Manurung, Nuriadi Marpaung, Nasrun Maulana Abdullah, Rafli Maulana, Cecep Maulidia, Annisa Miftahurridho, A Muhammad Toha Mika Gobai Milladah, Roshina Ayu Mohamad, Effendi Mohd Asrah, Norhaidah Muhammad Amin Muhammad Idris Muhammad Zulkarnain Mulya, Anugrah Hadi Mustika, Donny Muthia Dewi Nadhifah, Naurah Narda Widiada, I Gde Nazri , M. Nazri Utama Hasibuan Nirda Julianda Noor Akma Ibrahim Nugraha, Daffa Yulistian Arienta Nugraha, Syechu Dwitya Nurdiyanti, Eni Nurfadillah, Dian Oktavia, Sella Oktaviani, Fitrotin Nafisa Popova, Solomiya Pragina, Baiq Rani Mayani Proboningtyas, Dewinta Dwi Purnomo, Julian Putih, Bening Putri Permata Rachma Prilian Eviningsih, Rachma Prilian Rakhmawati, Renny Renny Rakhmawati, Renny Rakhmawati Resmi Hutasoit Rinaldi Rinaldi Riswanto, Sigit Robo, Salahuddin Rochmawati, Reni Rochmawati, Reny Rusli, Muhammad Rizani Sa'ad, Asmadi sahren, sahren SANTOSO SANTOSO Sarah, Sophia Saripah Sobah Septi Yanaratri, Diah Sila, Ardi Azis Siswandi, Hendra Sitorus, Pangeran Holong SOPIAN SAORI Sosiawan Nusifera Sriyanto Sriyanto Sudarmin Sudarmin Suharyanto, Hendik Eko Hadi Sumarna, Ade Suradi Suradi Suryono Suryono Sutedjo Sutedjo Syah Alam Syakbani, Baehaki Syarifuddin, Rizal Tokoro, Yan Piet Triyasa, Ni Kadek Rika Mega Wibowo, Rezky A. Widiada, I Gde Narda Widiyanti, Hafsah Wilyus Wilyus Wirjaya, Salsabila Yanarat, Diah Septi Yanaratri, Diah Septi Yane R. Maahury Yeni Irawati Roragabar Yofsan Tolanda Yohanes Bandong Zulkarnain Zulkarnain Zuriati, Zuriati