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All Journal Jurnal Sains dan Teknologi: Jurnal Keilmuan dan Aplikasi Teknologi Industri Jurnal Teknik dan Informatika Journal of Applied Data Sciences Majalah Ilmiah UPI YPTK Journal of Computer Scine and Information Technology KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Abdimas ADPI Sains dan Teknologi Jurnal Ipteks Terapan : research of applied science and education Jurnal Sains Informatika Terapan (JSIT) Jurnal Komtekinfo Jurnal Teknik dan teknologi tepat guna
Dinul Akhiyar
Universitas Putra Indonesia “YPTK” Padang
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Adaptasi spesifik efficientnetb0 dengan lapisan kustom untuk identifikasi buah tropis Akhiyar, Dinul; Herasmus, Hilda; Nofriadiman
Jurnal Sains Informatika Terapan Vol. 4 No. 2 (2025): Jurnal Sains Informatika Terapan (Juni, 2025)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v4i2.727

Abstract

Identifikasi buah tropis berbasis citra digital menghadapi tantangan multidimensi akibat keragaman morfologi intra-kelas (seperti variasi tingkat kematangan pada pisang) dan kesamaan visual antar-kelas (misalnya kemiripan geometris antara mangga dan nanas), diperparah oleh kondisi lingkungan lapangan yang tidak terkontrol seperti pencahayaan dinamis dan latar belakang kompleks. Untuk mengatasi masalah ini, penelitian ini mengusulkan strategi adaptasi spesifik domain pada arsitektur EfficientNetB0 melalui integrasi blok lapisan kustom yang terdiri dari Dense Layer 256-neuron dengan aktivasi swish, normalisasi lapisan (Layer Normalization), dan Spatial Dropout 0.3, serta mekanisme kalibrasi bertahap (gradual unfreezing) yang membuka lapisan konvolusional secara progresif. Dataset sebanyak 5.200 citra buah tropis Indonesia (pisang, mangga, nanas, durian, rambutan) diperkuat dengan teknik augmentasi dinamis berbasis domain knowledge, termasuk color jitter terarah dan random erasing untuk meniru variasi kondisi riil. Hasil eksperimen menunjukkan pencapaian akurasi validasi 88.7% dan F1-score rata-rata 0.87, yang mengungguli kinerja MobileNetV2 sebesar 6.4% dalam uji komparatif. Implementasi operasional dalam sistem FruitScan-ID membuktikan efektivitas metode ini dengan mengurangi kesalahan identifikasi manual hingga 40%, menawarkan solusi komputasi tepi (edge-computing) yang hemat sumber daya untuk otomasi industri pertanian tropis.
Penilaian Tingkat Kepuasan Masyarakat Terhadap Pelayanan Pada Kantor Wali Nagari Bunga Pasang Salido Menggunakan Algoritma K-Means Hidayatullah, Genta Magribi; Putra, Ondra Eka; Rahmi, Nadya Alinda Rahmi; Akhiyar, Dinul
Jurnal Teknik dan Teknologi Tepat Guna Vol. 4 No. 1 (2025): Jurnal Teknik dan Teknologi Tepat Guna
Publisher : Riset Sinergi Indonesia

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Abstract

Assessing public satisfaction is crucial for evaluating the quality of services provided by government agencies. The purpose of this study was to analyze public satisfaction with the services provided by the Bunga Pasang Salido Village Head Office. This study used the K-Means algorithm, a clustering technique commonly used in data analysis, to categorize respondents' satisfaction levels. This algorithm effectively categorizes respondents into categories such as dissatisfied, satisfied, and very satisfied based on their responses in a satisfaction survey. The dataset used in this study was derived from questionnaires completed by 61 respondents who interacted with the population administration service. The questionnaire data covered various aspects of service quality, including service process efficiency, officer attitudes, and overall user experience. The results of the clustering analysis showed that 27.87% of respondents were dissatisfied, 42.62% were satisfied, and 29.51% were very satisfied with the services provided. This classification provides valuable insights into areas for improvement and helps the Bunga Pasang Salido Village Head Office improve service quality. This study highlights the importance of using data-driven methods, such as the K-Means algorithm, in improving public sector performance and overall service quality.
Decision Support System uses Multi-Objective Optimization By Ratio Analysis (MOORA) Method in Selection of the Best Herbal Medicine Supplier Buyung Septyanta, Andriyan; Akhiyar, Dinul
Journal of Computer Scine and Information Technology Volume 10 Issue 1 (2024): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v10i1.94

Abstract

Jamu is a traditional medicine made from natural cultural heritage that has been passed down from generation to generation for health. Mbak Sum UKM is a small and medium enterprise that operates in the field of medicines, namely herbal medicine, where this business provides various types of herbal medicine. This herbal medicine business is a business that can promise business opportunities. Because there is so much interest in this natural herbal concoction, it makes it difficult for companies to meet product availability. And this makes SMEs need many suppliers to meet product availability for Mbak Sum's SMEs. Therefore, a system is needed to help Mbak Sum's SMEs overcome the problems they face. The system that will help Mbak Sum's UKM is a decision support system in selecting quality suppliers which will later help in fulfilling herbal herbal products in Mbak Sum's UKM as well as in making reports of incoming products from suppliers to Mbak Sum's UKM. The system built to support supplier selection decisions uses the multi-objective optimization by ratio analysis (MOORA) method. The MOORA (Multi-Objective Optimization On The Basis Of Ratio Analysis) method is a multi-objective optimization technique that can be successfully applied to solve various types of complex decision-making problems in decision making. The results obtained were that the first rank was Alternative 3 with a value of 0.284 and the sixth rank was Alternative 6 with a value of 0.164. The calculation process can be concluded that A3 is the best alternative
Expert System for Diagnosing Malnutrition Using the Certainty Factor Method Hakim, Wijaya; Sumijan; Akhiyar, Dinul
Journal of Computer Scine and Information Technology Volume 10 Issue 1 (2024): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v10i1.95

Abstract

Malnutrition in toddlers causes a negative impact on motor nerve development, inhibits behavioral and cognitive development causing a decrease in academic performance and social skills . In addition, malnutrition during infancy can cause long-term risks that focus on later in life, increasing the risk of disease or disability or even death. With advances in information technology today, it is very helpful in predicting or identifying an event, one of which is an expert system that can help an expert in identifying a disease in the world of medicine. Therefore, an expert system is needed that can help doctors and the public find out the type of malnutrition they are suffering from based on the symptoms they are experiencing. The expert system uses the Certainty Factor method in reasoning to obtain diagnostic results from the symptoms shown. This method uses the value of an expert's belief in the symptoms of a disease. The aim of this research is to apply the certainty factor method in identifying malnutrition and providing definitions and suggestions for the disease suffered. The expert system was built using PHP and MySQL database. The results of applying the Certainty Factor method based on the tested data showed that the disease suffered by the patient was Kwarshiorkor with a Certainty Factor level of 0.958528 or 95%. The results of this test show that the certainty factor method expert system is able to identify a disease based on the symptoms experienced
Sistem Pakar Identifikasi Hama dan Penyakit Tanaman Menggunakan Metode Certainty Factor dan Forward Chaining Firdaus; Marfalino, Hari; Akhiyar, Dinul
Jurnal Sains Informatika Terapan Vol. 5 No. 1 (2026): Jurnal Sains Informatika Terapan (Februari, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

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Abstract

Hama dan penyakit tanaman merupakan salah satu permasalahan utama yang dapat menurunkan produktivitas dan kualitas hasil pertanian. Keterbatasan pengetahuan petani dalam mengidentifikasi jenis hama dan penyakit berdasarkan gejala yang muncul sering menyebabkan penanganan yang kurang tepat. Penelitian ini bertujuan untuk merancang dan membangun sistem pakar yang mampu mengidentifikasi hama dan penyakit tanaman berdasarkan gejala yang dialami tanaman. Sistem pakar dikembangkan menggunakan metode Forward Chaining sebagai mekanisme penelusuran aturan dan metode Certainty Factor untuk menghitung tingkat keyakinan diagnosis berdasarkan kombinasi nilai keyakinan pakar dan pengguna. Data pengetahuan diperoleh dari aturan yang menghubungkan gejala dengan jenis hama atau penyakit tertentu. Hasil pengujian menunjukkan bahwa sistem mampu memberikan diagnosis hama dan penyakit tanaman beserta tingkat kepastian dan rekomendasi solusi secara informatif. Sistem ini diharapkan dapat membantu pengguna, khususnya petani, dalam melakukan identifikasi awal hama dan penyakit tanaman secara cepat dan akurat sebagai dasar pengambilan keputusan penanganan.
Development of Color Segmentation and Texture Analysis Algorithms for Early Detection of Green Vegetable Deterioration in Retail Environments Akhiyar, Dinul; Fitri, Iskandar; Nurcahyo, Gunadi Widi
Journal of Applied Data Sciences Vol 7, No 2: May 2026
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v7i2.1094

Abstract

Vegetable deterioration in retail environments is often accelerated by improper storage conditions, leading to quality degradation, economic losses, and reduced consumer trust. Early detection of deterioration is therefore essential to enable timely preventive actions before visible spoilage becomes severe. This study proposes an integrated image-based framework for early detection of spinach leaf deterioration by combining K-Means++ for robust color segmentation, Gray Level Co-occurrence Matrix (GLCM) for texture feature extraction, and Convolutional Neural Network (CNN) for classification. K-Means++ improves segmentation stability through optimized centroid initialization, GLCM captures subtle texture variations associated with early spoilage, and CNN enables accurate classification by learning complex visual patterns from segmented images. The dataset consists of 642 spinach leaf images captured under controlled lighting for initial calibration and under varying lighting conditions to simulate real-world retail environments. Experimental results show that the standard K-Means algorithm achieved an average classification accuracy of 77%, while the proposed K-Means++ segmentation improved accuracy to 81.86%. Furthermore, CNN-based validation achieved the highest classification accuracy of 94.82%, demonstrating strong generalization capability. The novelty of this work lies in the optimized integration of K-Means++ segmentation under lighting variability, selective GLCM feature utilization validated through ablation analysis, and end-to-end CNN-based validation with real-time deployment feasibility. The proposed framework offers a practical, scalable, and non-destructive solution for automated freshness monitoring in retail environments and can be extended to other leafy vegetables.
Co-Authors Abulwafa Muhammad Aqil Ramadhan, Muhammad Arsyah, Ulya Ilhami Buyung Septyanta, Andriyan Firdaus Gunadi Widi Nurcahyo Hakim, Wijaya Herasmus, Hilda Hidayatullah, Genta Magribi Irzal Arief Wisky Irzal Arif Wisky Iskandar Fitri, Iskandar Iskandar, Riki Mardhiah, Putri Marfalino, Hari Muhammad Akhif Muhammad, Abulwafa Nofriadiman Nori Sahrun Pratiwi, Mutiana Putra, Ondra Eka Radiyan Rahim Radiyan Rahim Rahim, Radiyan Rahmi, Nadya Alinda Rahmi Riki Iskandar Riki Iskandar Riki Iskandar Silfia Andini Sumijan Teri Ade Putra Vivi Nila Sari