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All Journal JOIV : International Journal on Informatics Visualization JOURNAL OF SCIENCE AND SOCIAL RESEARCH IT (INFORMATIC TECHNIQUE) JOURNAL Majalah Ilmiah UPI YPTK INFOSYS (INFORMATION SYSTEM) JOURNAL Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Komtekinfo Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) PUBLIDIMAS CSRID LEX JUSTITIA
Negoro, Wahyu Saptha
Universitas Potensi Utama
Aerospace Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Social Sciences

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JARINGAN SYARAF TIRUAN DENGAN LEARNING VECTOR QUANTIZATION (LVQ) UNTUK KLASIFIKASI DAUN: ARTIFICIAL NEURAL NETWORKS USING LEARNING VECTOR QUANTIZATION (LVQ) FOR LEAF CLASSIFICATION Soeheri; Sari, Rita; Wahyu Saptha Negoro; Yuhandri
CSRID (Computer Science Research and Its Development Journal) Vol. 16 No. 1 (2024): February 2024
Publisher : LPPM Universitas Potensi Utama

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

Abstract

Leaves are one part of a plant species that is commonly used to classify plant and plant species. The process of assisting various types of leaves usually involves experts using a herbarium, which is a collection of preserved plant specimens. Leaf classification is the detection of different types of leaves, where there are 2 types of leaves including Magnolia Soulangeana and Invillea leaves. The training data contains 30 images consisting of 15 each of the 2 types of leaves, then the test data contains 20 images which are also taken from the 2 types of leaves. So that the total images used are 50 leaf images. The leaf classification uses feature extraction and the method used in the classifier is Learning Vector Quantization (LVQ) which is a pattern classification method in which each output unit represents a particular class or group. The test results showed that the process of calling Magnolia Soulangeana and Bougainvillea leaves in this experiment was successful with 80% detection Keywords—Leaf classification, Learning Vector Quantization, Artificial Neural Networks, Feature extraction.
Computer Vision Technology Innovation Education to Support Early Warning Systems for Rice Diseases Saptha Negoro, Wahyu; Adinda Destari, Ratih; Hendra Azhar, Asbon; Syahrian, Achmad
Majalah Ilmiah UPI YPTK Vol. 32 (2025) No. 2
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jmi.v32i2.199

Abstract

Rice plant diseases are one of the main factors causing decreased productivity and threatening national food security. Farmers' limited knowledge in recognizing early symptoms of disease often leads to delays in treatment. The results of this research are educated in community service with the aim of developing and implementing Computer Vision-based technological innovation education to support an early warning system for rice diseases. The methods used include collecting rice leaf images in the field, digital image processing, and applying Computer Vision models to recognize visual patterns of disease symptoms. Educational activities with students are carried out through training and mentoring for farmers and agricultural extension workers regarding the use of this technology as an early detection tool. The expected results of this service are increased understanding and ability of users or partners in identifying rice diseases more quickly and accurately, so that they can support appropriate decision-making in disease control and increase rice agricultural productivity in a sustainable manner.
Combination of Active Contour and CNN-based Segmentation Methods to Improve Accuracy in Detecting Rice Diseases Saptha Negoro, Wahyu; Adinda Destari, Ratih; Hendra Azhar, Asbon; Syahrian, Achmad
Jurnal KomtekInfo Vol. 12 No. 4 (2025): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v12i4.671

Abstract

Rice diseases are one of the main factors causing decreased productivity and threatening national food security. The main problem in controlling rice diseases is the delay and inaccuracy of symptom identification in the field. This study aims to develop an artificial intelligence-based rice disease detection system through a combination of Active Contour and Convolutional Neural Network (CNN) methods. The research object is rice leaf images taken from rice fields in Pulau Sejuk Village, Batubara Medan, with a dataset of 600 images consisting of healthy leaves and 3 types of rice diseases. The Active Contour method is used in the segmentation stage to extract leaf areas precisely, while CNN is applied for the disease classification process. The results show that this combination of methods can significantly improve the accuracy of rice disease detection. The developed system is expected to assist farmers and stakeholders in the early detection of rice diseases, thereby supporting food innovation and increasing sustainable agricultural productivity.
Implementasi Tanggung Jawab Kepolisian Tentang Pengamanan Eksekusi Jaminan Fidusia Boby Daniel Simatupang; Wahyu Saptha Negoro; Ivo Ramadhani
Lex Justitia Vol 6 No 2 (2024): LEX JUSTITIA VOL. 6 NO. 2 JULI 2024
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/lj.6.2.2024.66-80

Abstract

Didalam aturan Pemerintah Indonesia sudah tertuang mengenai Fidusia pada Undag-Undang No. 42 tahun 1999. Dimana pengertian Fidusia yaitu suatu proses mengalihkan hak milik atas sesuatu benda dengan dasar kepercayaan, tetapi benda tersebut masih dalam penguasaan pihak yang mengalihkan. Sehingga Hukum menjadi sarana penciptaan suatu aturan masyarakat yang ada bagi semua orang yang ingin merasakan keadilan sehingga menjadi focus tujuan utama pembentukan perundang-undangan yang harus sesuai dengan prinsip-prinsip keadilan dan Fungsi hukum sebagai control sosial, maka hal ini hubungan hukum dengan nilai-nilai sosial adalah saling berkaitan dimana hukum sebagai penyelesaian masalah bagi masyarkat pada umumnya. Hukum memiliki fungsi rangkap disatu pihak merupakan tindakan yang mungkin demikian melembaga yang kemudian dipakai oleh masyarakat untuk mencapai suatu tujuan penyelesaian masalah dalam tanggung-jawab kepolisian tentang pengamanan Ekseskusi Jaminan Fidusia. Metode yang digunakan dalam penelitian ini adalah Metode Penelitian Terapan (Applied Research), dimana metode ini merupakan jenis penelitian yang hasilnya dapat secara langsung diterapkan untuk memecahkan permasalahan yang tengah dihadapi. Sebagaimana . Prof. Mr.E.M Meyers yang mengartikan Hukum dalam bukunya “ De Algemene bergrippen van het burglijik Recht “ : “Hukum adalah semua aturan yang mengandung pertimbangan kesusilaan, ditujukan kepada tingkah laku manusia dalam masyarakat, dan yang menjadi pedoman bagi penguasa-penguasa negara dalam melakukan tugasnya”. Dan teori Leon Duhuit : “ hukum ialah aturan tingkah laku para anggota masyarakat aturan yang daya penggunaanya pada saat tertentu diindahkan oleh suatu masyarakat sebagai jaminan dari reaksi bersama dan yang jika dilanggar menimbulkan reaksi bersama terhadap orang yang melakukan pelanggaran itu”Rumusan Masalah (1).Bagaimana sikap pemilik kendaraan yang masih kredit dalam menunggak pembayaran?;(2) Apa yang perlu dipersiapkan jika berjumpa dengan Debt Kolector Leasing;(3). Siapa yang berhak melakukan sita eksekusi fidusia. Apa yang dimaksud dengan hukum serta apa yang dimaksud dengan nilai dan nilai sosial?.
Classification and Interpretability of Employee Burnout Using Linear Discriminant Analysis Dwi Robiul Rochmawati; Muhammad Al Adib; Diyo Mollana Fazri; Bill Raj; Romi Antoni; Rahmad Santoso; Wahyu Saptha Negoro
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 3 (2026): Februari 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i3.811

Abstract

Employee burnout has become a critical challenge in modern organizations due to its negative impact on employees’ mental well-being, work performance, and organizational sustainability. In many workplaces, burnout identification still relies on subjective assessments and retrospective surveys, limiting the effectiveness of early intervention strategies. This study aims to develop an employee burnout risk classification model that achieves high predictive performance while maintaining strong interpretability. Linear Discriminant Analysis (LDA) is employed as the primary method because of its ability to separate classes optimally and provide explicit discriminant coefficients for explanatory analysis. The study utilizes a secondary dataset from the Mental Health in Workplace Survey, consisting of 3,000 employee records and 15 variables related to job characteristics, psychosocial factors, and individual conditions. The dataset is divided into training and testing sets with an 80:20 ratio. Experimental results show that the LDA model achieves an accuracy of 96.17%, with a precision of 89.50%, recall of 100%, F1-score of 94.46%, and an AUC value of 0.9988, indicating excellent classification capability. Further analysis of discriminant coefficients reveals that individual burnout indicators, job roles, work–life balance, and career growth opportunities are the most influential factors in determining burnout risk. These findings demonstrate that LDA offers an effective and interpretable approach for early burnout detection and supports evidence-based decision-making for human resource management.
Kombinasi K-Means dan Fuzzy C-Means untuk Clustering Transaksi PPOB Berdasarkan Validitas Cluster Nanda Setiawan; Heru Fredi; Bualazatulo Laia; Yiska Dayanti Zagoto; Johan; Andreas Jorghy Parapat; Wahyu Saptha Negoro
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 3 (2026): Februari 2026
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i3.839

Abstract

Perkembangan layanan Payment Point Online Bank (PPOB) telah mendorong peningkatan signifikan pada volume dan kompleksitas data transaksi digital yang dihasilkan. Algoritma K-Means merupakan salah satu metode clustering yang paling banyak digunakan karena kesederhanaan, efisiensi komputasi, dan kemampuannya dalam menangani data berskala besar. Tujuan penelitian adalah mengelompokkan data transaksi PPOB secara optimal menggunakan kombinasi algoritma K-Means dan Fuzzy C-Means (FCM) serta mengevaluasi kualitas cluster berdasarkan validitas cluster. Data yang digunakan dalam penelitian ini merupakan data transaksi Payment Point Online Bank (PPOB) periode Januari 2024 yang diolah menggunakan Google Colaboratory (Google Colab). Data tersimpan dalam format CSV dan berisi informasi transaksi yang dilakukan oleh berbagai loket PPOB dengan jumlah data: 498.853 data transaksi. Penerapan metode Fuzzy C-Means memberikan hasil yang lebih sesuai karena mampu merepresentasikan derajat keanggotaan ganda pada loket-loket yang berada di zona transisi antar cluster. Keberadaan zona transisi tersebut membuktikan bahwa pendekatan Fuzzy lebih tepat digunakan dalam konteks bisnis PPOB yang dinamis, di mana performa loket dapat berubah seiring waktu dan tidak selalu berada pada kategori yang bersifat mutlak.
Automated Fruit Image Classification Based on HSV Features, Morphological Segmentation, and Extreme Learning Machine Agung Ramadhanu; Halifia Hendri; Wahyu Saptha Negoro; Mardison Mardison; Larissa Navia Rani; Sofika Enggari; Muhammad Reza Putra
CSRID (Computer Science Research and Its Development Journal) Vol. 18 No. 1 (2026): Februari 2026
Publisher : LPPM Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/csrid-.18.1.2026.135-147

Abstract

Fruit image classification plays a crucial role in smart agriculture, particularly in automating sorting and quality control processes. This study proposes a fruit classification system by integrating HSV color space conversion, adaptive thresholding, morphological segmentation, and the Extreme Learning Machine (ELM) algorithm. The dataset consists of three fruit classes—apple, pineapple, and watermelon—with a total of 480 images, divided into 360 training samples and 120 testing samples. Image preprocessing involves resizing, HSV conversion, noise reduction through morphological operations, and feature extraction based on color and shape characteristics. The extracted features are used to train and test an ELM model. To improve classification performance and address potential overfitting in traditional ELM, this study introduces a new development called the Extended Extreme Learning Machine (EELM). The key innovation lies in modifying the calculation of the output weights βj, where a regularization term is introduced using ridge regression to stabilize learning and improve generalization. Experimental results show that the proposed system achieves 100% accuracy on the training data and an average accuracy of 83.3% on the testing data. The system also demonstrates robustness in handling varying lighting conditions and fruit shapes. These improvements enable EELM to better handle noisy or complex data by preventing over-reliance on randomly initialized hidden layer parameters. Consequently, EELM demonstrates improved reliability, making it more suitable for deployment in resourceconstrained real-world environments such as mobile or embedded systems.
Lossy Image Compression Analysis using JPEG Algorithm on Rice Leaf Disease Dataset Wahyu Saptha Negoro; Ratih Adinda Destari; Asbon Hendra Azhar; Fhery Agustin
Jurnal KomtekInfo Vol. 13 No. 2 (2026): Komtekinfo
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/komtekinfo.v13i2.697

Abstract

The development of digital image processing technology has increased the need for more efficient image data storage and transmission, especially in the field of smart agriculture that utilizes digital images as a source of information. Rice leaf disease image datasets generally have quite large file sizes, requiring compression techniques to save storage capacity and speed up the data exchange process. This study aims to analyze the performance of lossy image compression using the JPEG algorithm on rice leaf disease datasets by evaluating file size efficiency and the quality of the compressed images. The research process includes collecting rice leaf disease image datasets, applying JPEG compression at several quality levels (quality factors), and measuring performance using the Compression Ratio (CR), Peak Signal-to-Noise Ratio (PSNR), and Mean Squared Error (MSE) parameters. The analysis results show that the lower the quality factor value used, the greater the compression ratio obtained, but the visual quality of the image decreases as indicated by the increase in the MSE value and the decrease in the PSNR value. Conversely, a higher quality factor is able to maintain image quality with the consequence of a larger file size. The findings of this study indicate that the JPEG algorithm is able to provide a good compromise between storage efficiency and visual image quality so that it remains suitable for use in rice leaf disease datasets, especially as a preprocessing stage in image processing and artificial intelligence systems for plant disease classification.
Artificial Intelligence Education and Training Based on Machine Learning and Computer Vision to Improve Technological Literacy of Bina Satria Vocational School Students in Medan City Wahyu Saptha Negoro; Ratih Adinda Destari; Asbon Hendra Azhar; Fhery Agustin; Achmad Syahrian
Majalah Ilmiah UPI YPTK Vol. 33 (2026) No. 1
Publisher : Universitas Putra Indonesia YPTK Padang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35134/jmi.v33i1.221

Abstract

The development of Artificial Intelligence (AI) technology has had a significant impact on various sectors, including education. However, the level of technological literacy, particularly related to Machine Learning and Computer Vision, among students is still relatively low, so educational efforts are needed to improve students' understanding and skills in these technologies. This community service activity aims to improve the technological literacy of SMK Bina Satria Medan City students through practice-based education and training on the basic concepts of Artificial Intelligence, Machine Learning applications, and the implementation of Computer Vision using easily accessible software. The implementation method of this activity includes the stages of needs identification, material preparation, socialization, demonstration, direct practice using Google Colab, and evaluation through pre-tests and post-tests. The training materials include an introduction to Artificial Intelligence, types of Machine Learning, digital image processing, image classification using Convolutional Neural Networks (CNN), and a simple implementation of Computer Vision for object recognition. The results of the activity showed an increase in participants' knowledge and understanding of Artificial Intelligence concepts, as indicated by an increase in evaluation scores after the training. In addition, participants were able to independently implement a simple model for image classification using the Google Colab environment. This activity positively contributes to improving students' digital competencies while preparing them for technological advancements in the Industrial Revolution 4.0 and Society 5.0. It is hoped that similar activities can be implemented sustainably with broader coverage to support human resource development in the information technology sector.
KLASIFIKASI BIJI KOPI MENGGUNAKAN TEKNIK KOMBINASI RANDOM FOREST DAN INCEPTION V3 UNTUK EKSTRAKSI FITUR Lima Hartimar Rambe; Yuke Manza; Annisa Ashari; Wahyu Saptha Negoro
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.3976

Abstract

Abstract: Coffee bean classification is a crucial step in ensuring the quality and selling value of coffee products. Manual sorting methods are often inefficient and error-prone, necessitating a technology-based automated approach. This study proposes a combination of the Inception V3 architecture as an image feature extraction method and the random forest algorithm as a classifier to distinguish good and defective coffee beans. The dataset used consists of 986 images, divided into training and test data. The processing was carried out using the Orange Data Mining platform, which includes pre-processing, feature extraction, model training, and performance evaluation. The evaluation results show that the model produces an accuracy of 96.4% on the training data and 96.8% on the test data. In addition, other performance metrics such as AUC (1.000), F1-score (0.967), precision (0.968), recall (0.968), and MCC (0.922) strengthen the model's excellent classification performance. Thus, the combined approach of Inception V3 and random forest is proven effective and has the potential to be implemented in a digital image-based coffee bean classification system. Keywords: Coffee Bean Classification, Random Forest, Inception V3, Feature Extraction, Digital Imagery Abstrak: Klasifikasi biji kopi merupakan langkah penting dalam menjamin mutu dan nilai jual produk kopi. Metode manual dalam penyortiran sering kali tidak efisien dan rentan kesalahan, sehingga dibutuhkan pendekatan otomatis berbasis teknologi. Penelitian ini mengusulkan kombinasi arsitektur Inception V3 sebagai metode ekstraksi fitur citra dan algoritma random forest sebagai klasifikator untuk membedakan biji kopi bagus dan rusak. Dataset yang digunakan terdiri dari 986 gambar, terbagi menjadi data latih dan data uji. Proses pengolahan dilakukan menggunakan platform Orange Data Mining, yang meliputi tahap pra-pemrosesan, ekstraksi fitur, pelatihan model, dan evaluasi kinerja. Hasil evaluasi menunjukkan bahwa model menghasilkan akurasi sebesar 96,4% pada data latih dan 96,8% pada data uji. Selain itu, metrik performa lain seperti AUC (1.000), F1-score (0.967), precision (0.968), recall (0.968), dan MCC (0.922) memperkuat bahwa model ini memiliki kinerja klasifikasi yang sangat baik. Dengan demikian, pendekatan kombinasi Inception V3 dan random forest terbukti efektif dan berpotensi diimplementasikan dalam sistem klasifikasi biji kopi berbasis citra digital. Kata kunci: Klasifikasi Biji Kopi, Random Forest, Inception V3, Ekstraksi Fitur, Citra Digital
Co-Authors Achmad Syahrian Adinda Destari, Ratih Agung Ramadhanu Andreas Jorghy Parapat Andri Armaginda Siregar Annisa Ashari Arbana Syamanta Asbon Hendra Azhar Ashari, Annisa Azhar, Asbon Hendra Bill Raj Bob Subhan Riza Boby Daniel Simatupang Bualazatulo Laia Chan, Fajri Rinaldi Diki Ramanda Diyo Mollana Fazri Dwi Robiul Rochmawati Edy Victor Haryanto Edy Victor Haryanto Fhery Agustin Fragastia, Vidi Agung Fujiati Fujiati Halifia Hendri Hendra Azhar, Asbon Heru Fredi Idzhari Rahman Ivo Ramadhani Iwan Fitrianto Rahmad Jalaluddin Nasution Johan Larissa Navia Rani, Larissa Lima Hartimar Rambe Linda Wahyuni Manza, Yuke Mardison Mardison Mardison Mardison Mhd. Agung Irnanda Muhammad Al Adib Muhammad Reza Putra Nanda Setiawan Rahmad Santoso Rahmat Humala Putra Hasibuan Ramadhani, Ivo Rambe, Lima Hartimar Ratih Adinda Destari Ratih Adinda Destari, Ratih Adinda Rika Rosnelly Rini Oktari Batubara Rita Sari Rita Sari Romi Antoni Saiful Bukhori Simatupang, Boby Daniel Soeheri Soeheri Sofika Enggari Sumijan Sumijan Susiana Khosasih Syahrian, Achmad Yasmin, Nabilla Yiska Dayanti Zagoto Yuhandri Yuhandri, Yuhandri Yuke Manza