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All Journal Techno.Com: Jurnal Teknologi Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING SINTECH (Science and Information Technology) Journal Martabe : Jurnal Pengabdian Kepada Masyarakat Jambura Journal of Informatics Jurnal Tekno Kompak Jurnal Teknik Informatika (JUTIF) Journal of Computer and Information System (J-CIS)
Firgiawan, Wawan
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Agriculture, Biological Sciences & Forestry Arts Humanities Computer Science & IT Education Engineering Social Sciences

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PEMBERDAYAAN MASYARAKAT DESA BALOMBONG MELAWAN STUNTING MELALUI PENGOLAHAN MP-ASI BERBASIS PANGAN LOKAL DENGAN INOVASI ANDROID Arifin, Nurhikma; Firgiawan, Wawan; Fauziah, Fauziah
Martabe : Jurnal Pengabdian Kepada Masyarakat Vol 7, No 11 (2024): MARTABE : JURNAL PENGABDIAN MASYARAKAT
Publisher : Universitas Muhammadiyah Tapanuli Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31604/jpm.v7i11.4871-4883

Abstract

Stunting merupakan salah satu masalah kesehatan yang mendesak di Indonesia, termasuk di Desa Balombong, Sulawesi Barat, dengan angka prevalensi mencapai 40,6% pada tahun 2022, dua kali lipat dari standar WHO. Penyebab utama stunting di desa ini adalah kurangnya gizi seimbang selama masa pertumbuhan anak serta keterbatasan akses ibu balita terhadap informasi gizi dan cara mengolah makanan pendamping ASI (MP-ASI). Untuk itu kegiatan pengabdian ini bertujuan untuk meningkatkan keterampilan ibu balita dalam mengolah MP-ASI berbasis pangan lokal dan memperkenalkan aplikasi SIBAYI sebagai sumber informasi gizi. Pelatihan ini meliputi pengenalan stunting, dampaknya, serta solusi berupa penggunaan MP-ASI yang tepat dan bernutrisi. Demonstrasi pembuatan MP-ASI berbahan lokal, seperti ikan laut, serta pelatihan penggunaan aplikasi SIBAYI dilakukan untuk membantu ibu balita mengakses resep sehat dan informasi gizi anak. Evaluasi awal, proses, dan akhir dilakukan untuk mengukur peningkatan pemahaman dan keterampilan peserta. Hasilnya menunjukkan adanya peningkatan signifikan dalam pengetahuan mengenai stunting, pemanfaatan bahan pangan lokal, serta penggunaan teknologi dalam pengelolaan gizi anak. Meskipun beberapa peserta masih menghadapi tantangan dalam penggunaan aplikasi, pelatihan ini berhasil meningkatkan kepercayaan diri dan kemampuan ibu balita dalam menyediakan MP-ASI yang bergizi. Program ini diharapkan dapat berkontribusi dalam penurunan angka stunting di Desa Balombong dengan dukungan berkelanjutan dari semua pihak.
Implementasi Algoritma Round Robin dalam Sistem Multi-agent dan Multi-client untuk Load balancing Dinamis pada Jaringan Lokal Nopiana, Wiwi; Firgiawan, Wawan; Mansyur, Muh. Fuad
Techno.Com Vol. 24 No. 4 (2025): November 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i4.12777

Abstract

Load balancing merupakan mekanisme penting dalam sistem layanan web untuk menjamin pemerataan beban kerja dan menjaga kestabilan performa layanan. Penelitian ini mengimplementasikan algoritma Round Robin dalam arsitektur sistem multi-agent dan multi-client yang dijalankan pada local area networking (LAN). Sistem dirancang menggunakan tiga komputer, di mana satu komputer berperan sebagai agent controller yang menjalankan logika Round Robin, dan dua komputer lainnya sebagai server backend. Beberapa client dalam jaringan mengirimkan permintaan secara simultan ke controller, yang kemudian secara bergiliran mendistribusikan permintaan tersebut ke server menggunakan konfigurasi load balancing berbasis NGINX. Pengujian dilakukan dalam tiga skenario beban, yaitu 50, 100, dan 200 permintaan. Hasil pengujian menunjukkan bahwa sistem mampu mendistribusikan permintaan secara merata antara dua server backend, serta menghasilkan waktu respons yang stabil pada skenario beban ringan hingga sedang. Kinerja sistem tetap berada dalam batas wajar meskipun jumlah permintaan meningkat. Sistem ini menunjukkan karakteristik modular, ringan, dan mudah diimplementasikan dalam lingkungan terbatas, serta memiliki potensi untuk dikembangkan lebih lanjut dengan integrasi algoritma adaptif dan sistem pemantauan otomatis. Kata kunci - Load balancing, Round Robin, sistem multi-agent, multi-client, jaringan local
Performance Analysis of KNN and BERT Algorithms for Classifying Student Sentiments Towards Campus Services Mutmainna, Mutmainna; Hamrul, Heliawati; Firgiawan, Wawan
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11365

Abstract

This study addresses the limitations of campus service evaluation processes that are still conducted manually and are unable to optimally process students’ textual opinions. The objective of this research is to analyze and compare the performance of the K-Nearest Neighbor (KNN) and BERT algorithms in classifying student sentiments toward campus services. The research stages include text preprocessing, the generation of IndoBERT embeddings for the KNN model, and fine-tuning IndoBERT for direct sentiment classification. The dataset consists of student evaluation texts from the Faculty of Engineering at UNSULBAR, labeled as negative, neutral, and positive sentiments. Model evaluation is performed using accuracy, precision, recall, and F1-score metrics. The results show that the basic KNN model achieves an accuracy of 79%, while KNN with hyperparameter tuning improves performance to 86%. The BERT model delivers the best performance, achieving an accuracy of 88.68%, precision of 87.87%, recall of 90.19%, and an F1-score of 88.79%. These findings indicate that transformer-based approaches, particularly IndoBERT, are more effective in understanding the contextual nuances of student language than traditional methods, and are therefore more recommended for sentiment analysis implementation in campus service evaluation.
Perbandingan SVM dan CNN MobileNetV2 untuk Klasifikasi Residu Insektisida pada Citra Buah Kakao Rahmawati, Rahmawati; Arifin, Nurhikma; Firgiawan, Wawan
Jambura Journal of Informatics VOL 8, N0 1: APRIL 2026
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/jji.v8i1.37972

Abstract

The decline in cocoa production in West Sulawesi due to pest attacks and the use of insecticides that leave residues on the fruit surface has reduced visual quality and highlights the need for efficient automatic classification based on digital image processing. This study aims to classify cocoa fruit images into three classes (Normal, Insecticide-Treated, and Residue) and to compare the performance of Support Vector Machine (SVM) and Convolutional Neural Network (CNN) with the MobileNetV2 architecture. The dataset consists of 672 images divided into training and testing sets with an 80:20 ratio and evaluated under two training data conditions: imbalanced and balanced through rotation-based augmentation at an image size of 224×224 pixels. For SVM, color and texture features are extracted using Hue Saturation Value (HSV) and Local Binary Pattern (LBP), while the CNN model adopts MobileNetV2 with transfer learning and an adjusted fully connected layer. The results show that SVM with combined HSV and LBP features achieves an accuracy of 86.67%, whereas CNN attains 82.22% on data without augmentation and improves to 87.41% on augmented data. The McNemar test on the same test set yields p-values of 0.6171 and 1.0000 for the imbalanced and balanced training data conditions, indicating that the performance difference between the two methods is not statistically significant and that both models provide comparable classification capability.Penurunan produksi kakao di Sulawesi Barat akibat serangan hama dan penggunaan insektisida yang meninggalkan residu pada permukaan buah menurunkan kualitas visual dan menunjukkan perlunya metode klasifikasi otomatis berbasis pengolahan citra digital yang efisien. Penelitian ini bertujuan mengklasifikasikan citra buah kakao ke dalam tiga kelas (Normal, Berinsektisida, dan Residu) serta membandingkan kinerja Support Vector Machine (SVM) dan Convolutional Neural Network (CNN) dengan arsitektur MobileNetV2. Dataset terdiri atas 672 citra yang dibagi menjadi data latih dan data uji dengan rasio 80:20 dan dievaluasi pada dua kondisi data latih, yaitu tidak seimbang dan seimbang melalui augmentasi rotasi dengan ukuran citra 224×224 piksel. Pada SVM, fitur warna dan tekstur diekstraksi menggunakan Hue Saturation Value (HSV) dan Local Binary Pattern (LBP), sedangkan CNN menggunakan MobileNetV2 dengan pendekatan transfer learning dan penyesuaian fully connected layer. Hasil pengujian menunjukkan bahwa SVM dengan kombinasi fitur HSV dan LBP mencapai akurasi 86,67%, sedangkan CNN memperoleh akurasi 82,22% pada data tanpa augmentasi dan meningkat menjadi 87,41% pada data setelah augmentasi. Uji McNemar pada data uji yang sama menghasilkan nilai p-value 0,6171 dan 1,0000 untuk kondisi data latih tidak seimbang dan seimbang, yang menunjukkan bahwa perbedaan performa kedua metode tidak signifikan secara statistik sehingga keduanya memiliki kemampuan klasifikasi yang relatif sebanding.
Buffalo Price Estimation Using YOLOv8 And Image Thresholding Amelia, Amelia; Firgiawan, Wawan; Sulfayanti, Sulfayanti
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 1 (2026): JUTIF Volume 7, Number 1, February 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.1.4749

Abstract

The skin color pattern of buffaloes can determine their market price, especially for traditional ceremonial purposes that involve buffaloes. Currently, the pricing of buffaloes is still done subjectively by sellers or buyers, resulting in inconsistencies in price determination. This study proposes the development of a system to estimate the price of buffaloes based on their type and the percentage of light and dark skin, specifically for the Saleko buffalo type. The algorithm used to recognize buffalo types is YOLOv8, which was trained to detect four classes: Lotongboko, Saleko, Bonga, and Other types. The model was trained over 100 epochs using the Adam optimizer and hyperparameters. A thresholding method was applied to identify the percentage of black and white on the Saleko buffalo images that were successfully detected by YOLOv8. If the light skin percentage exceeds 80%, the buffalo is estimated to be worth 800 million rupiah. Otherwise, the Saleko buffalo is estimated at 300 million rupiah. The YOLOv8 training achieved a highest mAP value of 97.8%, with steadily decreasing loss and increasing metrics at each iteration, indicating a successful training process with strong detection performance. The price estimation model achieved an accuracy of 76.3% based on 55 tested images. Estimation errors were caused by low image resolution and poor lighting quality. This study provides insights into the application of technology for buffalo price estimation through digital image processing.
Comparison of SVR Parameter Optimization Using Particle Swarm Optimization (PSO) and Random Search for Rice Harvest Yield Prediction Yumeivia, Narlin; Wajidi, Farid; Firgiawan, Wawan
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5509

Abstract

Rice yield is an important part in a precision agriculture system that can support farmers' decision-making in a more targeted manner. The author's research aims to help farmers and stakeholders in Bambang Village predict crop yields accurately to overcome production fluctuations. Through appropriate efforts and strategies, this technology is expected to improve food security and farmer welfare. The research method uses the Support Vector Regression (SVR) algorithm for the modeling process, with the help of Particle Swarm Optimization (PSO) and Random Search optimization in finding the best parameters. The research dataset includes 1,120 historical data of rice harvests in Bambang Village for the 2022–2023 period tested through 70:30 and 60:40 data sharing scenarios. Model performance is evaluated using the Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R2) metrics. The MAPE metric is used as the main indicator of relative accuracy by measuring the average percentage deviation between predicted values and actual values; a low MAPE value is very significant because it reflects the model has a minimal error rate on a percentage scale, thus providing more precise estimates for farmers. The results showed that both optimization methods successfully identified SVR parameters (C, gamma, epsilon) that followed the data trend. Random Search produced slightly superior R2 performance (reaching 82.20% at a 60:40 ratio), while PSO showed more consistent parameter exploration stability. These findings demonstrate that the integration of machine learning and optimization techniques has great potential in strengthening data-driven agricultural systems to improve food security and farmer welfare.
Performance Comparison Of K-Nearest Neighbors And Decision Tree Algorithms With Random Oversampling For Imbalanced Heart Disease Classification Yustianisa, Dita; Wajidi, Farid; Firgiawan, Wawan; Sholeha, Adinda Gama
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5626

Abstract

Heart disease remains one of the leading causes of mortality worldwide, including in Indonesia, where delayed detection continues to be a serious challenge. In medical data mining, class imbalance often degrades classification performance by reducing sensitivity toward minority class cases. This study aims to compare the performance of the K-Nearest Neighbors (KNN) and Decision Tree algorithms for heart disease classification and to evaluate the effectiveness of random oversampling in handling imbalanced data. This research uses a heart disease dataset consisting of 10,000 medical records obtained from Kaggle. Data preprocessing includes categorical transformation, missing value imputation using KNN Imputer, and Min–Max normalization. Random oversampling is applied to increase minority class representation. Model evaluation is performed using stratified 10-fold cross-validation with accuracy, precision, recall, F1-score, and Receiver Operating Characteristic–Area Under the Curve (ROC–AUC) as performance metrics. Experimental results show that after random oversampling, the KNN model achieves the best performance with an accuracy of 94%, precision of 96%, recall of 90%, F1-score of 92%, and ROC–AUC of 90.2%. In comparison, the Decision Tree model records an accuracy of 80%, precision of 78%, recall of 81%, F1-score of 79%, and ROC–AUC of 81.5%. These findings demonstrate that random oversampling significantly improves minority class detection, particularly for KNN. This study contributes to Informatics by providing empirical evidence that simple and efficient data mining strategies can effectively address class imbalance in large-scale medical datasets, supporting the development of accurate, interpretable, and accessible AI-based diagnostic systems for early heart disease detection.
Co-Authors Aditia, Muhammad Ahyar, Muh. Amelia Amelia Arifin, Nurhikma Farid Wajidi Fauziah Fauziah Fauziah Fauziah Gabrelia, Gabrelia Ghadavi, Gibran Halisah, Nur Hamrul, Heliawati Hasminarni Irianti, Arnita M. Darwin Mansyur, Muh. Fuad Maulidia, Ulfa Muhammad Furqan Rasyid Mutmainna, Mutmainna Nabila, Putri Nina Nina Nirwana, Hafsah Nopiana, Wiwi Nur, Nurrahmi Afiah Nuralamsah Zulkarnaim Rahmawati, Rahmawati Rasyid, Muh. Rafly RR. Ella Evrita Hestiandari Sholeha, Adinda Gama Sulfayanti, Sulfayanti Syam, Nur Aini Usran, Usran Yumeivia, Narlin Yustianisa, Dita Zahir Zainuddin