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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

Show Abstract | Download Original | Original Source | Check in Google Scholar

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.

Implementation Of Deep Learning Using Convolutional Neural Network Method In A Rupiah Banknote Detection System For Those With Low Vision Akhiyar, Dinul; Tukino, Tukino; Defit, Sarjon
ILKOM Jurnal Ilmiah Vol 17, No 1 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i1.2253.34-43

The application of deep learning in various sectors continues to grow due to its ability to provide efficient and effective solutions to complex problems. One significant implementation is in object detection, such as identifying Indonesian rupiah banknotes. This innovation aims to assist individuals with visual impairments in using money more effectively. At present, visually impaired individuals rely on conventional methods, such as identifying banknotes by touch, folding them in specific ways, or seeking assistance from others. However, these methods are often time-consuming, prone to error, and lack practicality in everyday situations. In this project, a system was developed using the Convolutional Neural Network (CNN) architecture combined with the YOLO (You Only Look Once) algorithm. YOLO is renowned for its speed and accuracy in real-time object detection, making it an ideal choice for detecting banknotes in moving images. The training dataset included 1,260 images, and the model underwent 7,000 iterations during training. As a result, the system achieved a high mean Average Precision (mAP) score of 97.65%, demonstrating its robustness and precision. For validation, 140 test images were utilized, which yielded an impressive mAP value of 97.5%. To further evaluate the system's reliability, tests were conducted under varying conditions, such as banknotes with creases, folds, or different lighting scenarios. These tests resulted in an mAP score of 88%, showcasing the system's adaptability to real-world conditions. This system provides significant benefits for individuals with visual impairments by offering a practical, efficient, and accurate solution for recognizing banknotes. With this technology, visually impaired users can interact with currency independently, reducing their reliance on others and traditional, less practical methods. This innovation not only enhances their autonomy but also fosters inclusivity in financial transactions. By integrating this system into mobile applications or wearable devices, its accessibility and usability can be further improved, paving the way for a broader societal impact.

Implementation of a Decision Support System for Contract Employee Recruitment Selection using the Simple Multi Attribute Rating Technique (SMART) Method Muhammad Akhif; Silfia Andini; Dinul Akhiyar
Journal of Computer Scine and Information Technology Volume 9 Issue 3 (2023): JCSITech
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jcsitech.v9i3.82

LPMP West Sumatra is a company in the technical implementation department of the Ministry of Education and is led by leadership and is responsible to the Director General for Improving the Quality of Educators and Education Personnel (PMPTK). The selection process for hiring contract employees has difficulties because the system is still manual where all the processes from the initial stage of registration selection to the final stage of registration selection are all done manually so it takes quite a long time and is troublesome and drains a lot of energy and energy for the company management. . So there is a need for an online selection information system for contract employee recruitment at LPMP West Sumatra such as a decision support system. This Decision Support System (DSS) uses the Simple Multi Attribute Rating Technique (SMART) method. The SMART method is a decision-making method for handling multi-criteria problems based on calculating the criteria weights for each alternative. The aim of this decision support system was to be able to assist the selection process for hiring contract employees at LPMP West Sumatra quickly, precisely and accurately. The results obtained by the alternative with the name of the prospective contract employee were Ririn Novrianti with a score of 0.755. This helps West Sumatra LPMP leaders in determining prospective contract employees who meet the requirements for employment

Decision Support System uses Multi-Objective Optimization By Ratio Analysis (MOORA) Method in Selection of the Best Herbal Medicine Supplier Andriyan Buyung Septyanta; Dinul Akhiyar
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

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 Wijaya Hakim; Sumijan; Dinul Akhiyar
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

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

Development of Color Segmentation and Texture Analysis Algorithms for Early Detection of Green Vegetable Deterioration in Retail Environments Dinul Akhiyar; Iskandar Fitri; Gunadi Widi Nurcahyo
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

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.

Sistem Pakar Identifikasi Hama dan Penyakit Tanaman Menggunakan Metode Certainty Factor dan Forward Chaining Firdaus; Hari Marfalino; Dinul Akhiyar
Jurnal Sains Informatika Terapan Vol. 5 No. 1 (2026): Jurnal Sains Informatika Terapan (Februari, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i1.960

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.

Pemanfaatan Machine Learning Dalam Analisis Data Untuk Mendukung Pengambilan Keputusan Cerdas Berbasis Data Modern Dinul Akhiyar; Hari Marfalino; Radiyan Rahim
Jurnal Sains Informatika Terapan Vol. 5 No. 2 (2026): Jurnal Sains Informatika Terapan (Juni, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i2.1194

Perkembangan teknologi digital telah menghasilkan volume data yang sangat besar dan kompleks sehingga membutuhkan metode analisis yang lebih efektif dibandingkan pendekatan konvensional. Machine Learning merupakan salah satu cabang kecerdasan buatan yang mampu mengolah data dalam jumlah besar untuk menghasilkan informasi yang mendukung pengambilan keputusan secara cerdas. Penelitian ini bertujuan menganalisis pemanfaatan Machine Learning dalam proses analisis data modern serta kontribusinya terhadap peningkatan kualitas pengambilan keputusan. Metode penelitian yang digunakan adalah studi literatur dengan pendekatan deskriptif melalui pengumpulan berbagai sumber ilmiah terkait algoritma Machine Learning, analisis data, dan sistem pendukung keputusan. Hasil penelitian menunjukkan bahwa algoritma Machine Learning seperti Decision Tree, Random Forest, dan Neural Network mampu meningkatkan akurasi prediksi, mengidentifikasi pola tersembunyi, serta memberikan rekomendasi berbasis data secara otomatis. Implementasi teknologi ini telah diterapkan pada berbagai sektor seperti bisnis, kesehatan, pendidikan, dan keuangan. Dengan demikian, Machine Learning menjadi teknologi strategis yang mampu mendukung organisasi dalam menghasilkan keputusan yang lebih cepat, akurat, dan efektif di era data modern.

Optimalisasi Media Promosi Digital Bagi UMKM Lokal: Perancangan Video Branding Untuk Lokak Leather Craft Di Kota Padang Maharian Agung; Dinul Akhiyar; Egi Zulio Apruri
Jurnal Pengabdian Masyarakat Bangsa Vol. 4 No. 2 (2026): JURMAS BANGSA
Publisher : Riset Sinergi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jurmas.v4i2.1348

Sumatera Barat memiliki potensi kerajinan kulit yang melimpah, namun UMKM lokal seperti Lokak Leather Craft masih menghadapi tantangan dalam hal brand awareness dan persaingan dengan produk luar daerah. Kegiatan pengabdian ini bertujuan untuk mengoptimalkan media promosi digital melalui perancangan video branding bagi Lokak Leather Craft di Kota Padang. Metode pelaksanaan mencakup observasi, wawancara mendalam dengan founder, analisis SWOT, serta tahapan pra produksi, produksi, dan pasca produksi video. Hasil kegiatan berupa video promosi berdurasi 58 detik dengan konsep visual vintage dan maskulin yang mengangkat narasi nilai dompet kulit sebagai simbol kepercayaan diri. Video ini dirancang dengan pendekatan cinematic advertising dan storytelling untuk memperkuat brand identity Lokak Leather Craft. Kegiatan ini diharapkan dapat meningkatkan brand awareness, menarik minat pasar yang lebih luas, serta mendukung keberlanjutan UMKM kerajinan kulit di Sumatera Barat.

Sistem Pakar Identifikasi Hama dan Penyakit Tanaman Menggunakan Metode Certainty Factor dan Forward Chaining Firdaus; Hari Marfalino; Dinul Akhiyar
Jurnal Sains Informatika Terapan Vol. 5 No. 1 (2026): Jurnal Sains Informatika Terapan (Februari, 2026)
Publisher : Riset Sinergi Indonesia (RISINDO)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62357/jsit.v5i1.960

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.

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