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All Journal Techno.Com: Jurnal Teknologi Informasi Indonesian Journal of Artificial Intelligence and Data Mining Insect (Informatics and Security) : Jurnal Teknik Informatika Jurnal Eksplora Informatika Martabe : Jurnal Pengabdian Kepada Masyarakat Informatik : Jurnal Ilmu Komputer To Maega: Jurnal Pengabdian Masyarakat Jurnal Penelitian Enjiniring Fakultas Teknik Unhas Jurnal Teknik Informatika (JUTIF) Jurnal SAINTIKOM (Jurnal Sains Manajemen Informatika dan Komputer) Journal of Computer and Information System (J-CIS) Jurnal Minfo Polgan (JMP) Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Jurnal Sintaks Logika (JSilog) Ceddi Journal of Education Jurnal Pengabdian Pada Masyarakat Nobel Community Services Journal
Arifin, Nurhikma
Universitas Sulawesi Barat
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Klasifikasi Penyakit Pada Daun Tanaman Kubis Menggunakan Metode Support Vector Machine (SVM) Berdasarkan Warna Dan Tekstur Maulidia, Ulfa; Wajidi, Farid; Arifin, Nurhikma
Techno.Com Vol. 24 No. 3 (2025): Agustus 2025
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v24i3.13748

Abstract

Kubis merupakan salah satu komoditas pangan yang memiliki nilai ekonomi tinggi dan banyak dikonsumsi oleh masyarakat. Namun, hama dan penyakit lainnya adalah risiko terbesar dalam budidaya tanaman kubis. Salah satu faktor penting dalam keberhasilan produksi kubis adalah periode pertumbuhan, tetapi sering gagal karena banyak serangan hama. Penelitian ini bertujuan untuk mengklasifikasikan penyakit pada daun kubis menggunakan metode Support Vector Machine (SVM) berdasarkan fitur tekstur, yaitu Gray Level Coccurrence Matrix (GLCM) dan fitur warna Hue, Saturation, and Value (HSV) untuk memudahkan petani mengetahui jenis penyakitnya, sehingga dapat melakukan tindakan yang tepat untuk mencegah kerusakan lebih lanjut. Kumpulan data yang digunakan adalah 606 gambar daun kubis yang terbagi menjadi dua bagian, yaitu data latih dan data uji dengan rasio 80:20. Kumpulan data tersebut diklasifikasikan ke dalam lima kategori penyakit, yaitu: Bercak Cincin, Bercak Daun, Busuk Hitam, Jamur Berbulu Halus, dan Kutu Daun. Uji fitur GLCM dilakukan dengan membandingkan hasil percobaan sudut yaitu 0°, 45°, 90°, 135° dengan akurasi terbaik pada sudut 0°. Selain itu, parameter diuji pada metode SVM dengan kernel RBF, yaitu nilai C (1,5,10) dan gamma (10-1 – 10-5). Hasil akurasi terbaik menggunakan fitur GLCM dan HSV diperoleh dari nilai C = 10 dan gamma = 10-1 dengan akurasi 94,21%. Hal ini menunjukkan bahwa pengujian sudut fitur GLCM dan kernel RBF mempengaruhi hasil akurasi sehingga dalam penelitian ini penggunaan fitur GLCM dan HSV memberikan hasil yang lebih optimal. Proses klasifikasi juga memiliki waktu perhitungan yang relatif cepat, yaitu 1,90 detik. Kata kunci: Penyakit Daun Kubis, Gray Level Co-occurrence Matrix, Hue Saturation Value, Support Vector Machine, Kernel RBF
IoT-Enabled Real-Time Monitoring and Tsukamoto Fuzzy Classification of Mandar River Water Quality via Web Integration for Sustainable Resource Management Insani, Chairi Nur; Arifin, Nurhikma
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5249

Abstract

This study presents the design and implementation of a real-time water quality monitoring system that utilizes pH, Total Dissolved Solids (TDS), and turbidity sensors, integrated with an ESP32 microcontroller. Sensor data are processed using the Tsukamoto fuzzy logic method to classify river water suitability into two categories: Suitable and Not Suitable. This approach effectively addresses imprecise and uncertain data, thereby producing more reliable classifications compared to conventional threshold-based methods. System validation was conducted through field testing over seven consecutive days at four different times of the day (morning, midday, afternoon, and evening), with results demonstrating stable performance. Recorded pH values ranged from 7.02 to 9.96, TDS values from 140 to 176 ppm, and turbidity levels between 4.00 and 5.15 NTU, indicating that the Mandar River remains within safe limits for daily use. The novelty of this study lies in the direct implementation of the Tsukamoto fuzzy logic method on a resource-constrained IoT device (ESP32), enabling edge-level classification with low latency and without full reliance on cloud computing. The system is designed to maintain decision reliability even under fluctuating sensor data, thus offering a practical and integrated solution for real-time monitoring. The main contribution of this work to computer science is the demonstration of lightweight embedded intelligent algorithms capable of running on constrained devices, the reinforcement of Explainable AI through transparent linguistic rules, and the integration of IoT with edge computing to support sustainable resource management in real-time.
DETEKSI PENYAKIT DAUN CABAI MENGGUNAKAN KOMBINASI GLCM DAN HSV DENGAN KLASIFIKASI SVM Nurmadinah, Nurmadinah; Wajidi, Farid; Arifin, Nurhikma
Insect (Informatics and Security): Jurnal Teknik Informatika Vol. 11 No. 2 (2025): Oktober 2025
Publisher : Universitas Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33506/insect.v11i2.4820

Abstract

Chili pepper is one of the high-value horticultural commodities in Indonesia. However, this plant is highly susceptible to various leaf diseases such as yellow virus, leaf spots, leaf curl, nutrient deficiency, and whitefly infestation. Manual disease detection is often inaccurate and time-consuming, necessitating an automated solution that is more efficient and effective. This study aims to detect chili leaf diseases using texture and color features extracted from leaf images. This approach enables farmers to easily identify the type of disease affecting chili plants, allowing for faster and more precise control measures. The research utilizes 1,150 chili leaf images divided into five disease categories—yellow virus, leaf spot, leaf curl, nutrient deficiency, and whitefly—each consisting of 230 images (184 training and 46 testing data). Feature extraction is performed on color features using the Hue, Saturation, Value (HSV) color space and on texture features using the Gray-Level Co-occurrence Matrix (GLCM) method. For classification, the Support Vector Machine (SVM) with Radial Basis Function (RBF) kernel is employed. Parameter testing of C (1, 5, and 10) and Gamma (0.1, 0.01, 0.001, 0.0001, and 0.00001) shows that the best performance is achieved at angles 0° and 135°, with C=10 and γ=0.1, yielding a classification accuracy of 91.30%. These results indicate that the combination of GLCM and HSV features, along with optimal RBF kernel parameter tuning, effectively enhances classification accuracy.
Penentuan Takaran Pupuk Nitrogen Tanaman Padi Menggunakan Metode Histogram BWD Sari, Dian Megah; Insani, Chairi Nur; Heri, Adi; Arifin, Nurhikma
Eksplora Informatika Vol 13 No 2 (2024): Jurnal Eksplora Informatika
Publisher : Institut Teknologi dan Bisnis STIKOM Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30864/eksplora.v13i2.1002

Abstract

Padi merupakan komoditas tanaman pangan yang sejak dulu menjadi penghidupan bagi masyarakat Indonesia, menjadi tanaman pangan prioritas utama dan dikonsumsi masyarakat dalam kesehariannya sehingga perlu dijaga kualitasnya. Salah satu yang menandakan bahwa tanaman padi itu memiliki kualitas yang baik adalah dengan melihat warna dari daun padi tersebut, dimana semakin hijau warna daun padi maka akan semakin baik pula kualitas dan kesehatan padi, untuk tetap menjaga kualiatas tanaman padi maka diperlukan Pemberian pupuk, karena salah satu faktor utama yang dapat mempengaruhi kualitas padi menjadi semakin baik adalah dengan memberikan pupuk yang mengandung unsur hara dan dengan takaran yang seimbang. Untuk pemberian pupuk dengan takaran yang seimbang maka dibutuhkan pengawasan ataupun alat bantu ukur. Tujuan dari penelitian ini adalah membangun sebuah sistem untuk menentukan jumlah takaran pupuk nitrogen yang diukur berdasarkan warna daun pada tanaman padi. sistem dibangun menggunakan Bahasa Pemrograman Python dengan menerapkan Metode Histogram untuk mengimplementasi citra warna daun dari Bagan Warna Daun (BWD). Metode pengembangan sistem menggunakan metode prototype. Dalam metode prototipe, fokus utama adalah pada pembuatan prototipe awal yang dapat mensimulasikan fitur atau fungsi utama dari perangkat lunak yang akan dikembangkan.
LEAF DISEASE DETECTION IN TOMATO PLANTS USING XCEPTION MODEL IN CONVOLUTIONAL NEURAL NETWORK METHOD Arifin, Nurhikma; Maratuttahirah; Juprianus Rusman; Muhammad Furqan Rasyid
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 2 (2024): JUTIF Volume 5, Number 2, April 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.2.1926

Abstract

This study aims to detect leaf diseases in tomato plants by applying the Xception model in the Convolutional Neural Network (CNN) method. The study categorizes tomato conditions into three main categories: Early Blight, Late Blight, and Healthy. Early Blight is generally infected by specific pathogens that cause spots and damage in the early stages of plant growth, while Late Blight is infected by pathogens in the later stages of the growing season. Meanwhile, the healthy category indicates normal conditions without disease symptoms. The dataset used consists of 300 tomato images, with each category having 100 images. In the model training phase using the fit method in TensorFlow, 17 epochs were performed to teach the model to recognize patterns in tomato leaf disease images in the training dataset. The model testing results on 30 tomato leaf images showed an accuracy rate of 85.84%. This result indicates a positive indication that the developed CNN model performs well in detecting and classifying tomato leaf conditions. Thus, this research can contribute to improving the understanding and management of leaf diseases in tomato plants to support more productive and sustainable agriculture.
HORTICULTURE SMART FARMING FOR ENHANCED EFFICIENCY IN INDUSTRY 4.0 PERFORMANCE Arifin, Nurhikma; Insani, Chairi Nur; Milasari, Milasari; Rasyid, Muhammad Furqan
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 5 (2024): JUTIF Volume 5, Number 5, Oktober 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.5.2728

Abstract

Chili peppers and papayas are important horticultural commodities in Indonesia with high economic value. To enhance productivity and efficiency in cultivating these crops, the application of Smart Farming technology is crucial. This study evaluates the use of image processing and artificial intelligence in the pre-harvest and post-harvest processes for chili peppers and papayas. For the pre-harvest process, data from 50 images of ripe chili peppers on the plant were used. The counting of ripe chilies was performed using HSV color segmentation with two masking processes, resulting in an average accuracy of 82.58%. In the post-harvest phase, 30 images of papayas, consisting of 10 images for each ripeness category—unripe, half-ripe, and ripe—were used. Papaya ripeness classification was carried out using the Support Vector Machine (SVM) algorithm with a Radial Basis Function (RBF) kernel and parameters C = 10 and γ = 10-3, achieving perfect classification accuracy of 100% for all categories. This study underscores the significant potential of Industry 4.0 technologies in enhancing agricultural practices and efficiency in the horticultural sector, providing important contributions to optimizing chili pepper and papaya production.
Comparison of SVM and Gradient Boosting with PCA for Website Phising Detection Syam, Nur Aini; Arifin, Nurhikma; Firgiawan, Wawan; Rasyid, Muhammad Furqan
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4344

Abstract

The increasing use of the internet has led to a rise in phishing attacks, posing a threat to user data security. This study compares the performance of the Support Vector Machine (SVM) and Gradient Boosting algorithms, integrated with Principal Component Analysis (PCA) for dimensionality reduction, in classifying phishing websites. The dataset consists of 11,054 samples classified into two categories: phishing (1) and non-phishing (-1), with three data partition scenarios for training and testing: 70:30, 80:20, and 90:10. Experimental results indicate that SVM outperforms Gradient Boosting in terms of accuracy and recall, particularly in detecting phishing websites. In the 80:20 and 70:30 data partition scenarios, the SVM model achieved an accuracy of 96% to 97% and had a higher recall for phishing websites, making it more sensitive to phishing detection. However, Gradient Boosting demonstrated consistent performance with an accuracy of around 94%, providing a balanced result between precision and recall for both classes. Therefore, the SVM model is superior for phishing detection tasks requiring high sensitivity to phishing websites, while Gradient Boosting remains a viable alternative when a more balanced performance between phishing and non-phishing sites is needed. The study concludes that both algorithms can be effectively used for phishing detection, with potential improvements through further experiments and hyperparameter tuning.
Corn Leaf Diseases Classification Using CNN with GLCM, HSV, and L*a*b* Features Johari, Putri Fausyah; Arifin, Nurhikma; Muzaki, Muzaki; Utama, Muhammad Surya Alif
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4345

Abstract

Corn leaf diseases can damage plants and reduce crop yields, thus affecting the quality and quantity of corn production. This study aims to classify corn leaf diseases using the Convolutional Neural Network (CNN) method with different color features, namely Gray Level Co-Occurrence Matrix (GLCM), HSV, and L*a*b*. The dataset consists of 1,739 corn leaf images, which are divided into four disease classes: Blight, Common Rust, Gray Spot, and Healthy. The data is split into training and testing sets using an 80:20 ratio. Two testing scenarios were conducted: individual feature evaluation and feature combination. The results show that in the first scenario, the L*a*b* feature provides the best accuracy at 91.75%, followed by the HSV feature with an accuracy of 90.29%, and GLCM with an accuracy of 78.40%. In the second scenario, the combination of HSV and L*a*b* features results in the highest accuracy of 92.48%, indicating that combining color and brightness information can improve the model's performance. The combination of GLCM and L*a*b* features results in an accuracy of 91.75%, while the combination of GLCM and HSV results in an accuracy of 90.29%. These findings demonstrate that integrating HSV and L*a*b features enhances CNN performance in corn leaf disease classification, outperforming individual feature- based approaches, thus contributing to more effective AI-based agricultural disease diagnosis.
Comparative Analysis of CNN, SVM, Decision Tree, Random Forest, and KNN for Maize Leaf Disease Detection Using Color and Texture Feature Extraction Arifin, Nurhikma; Insani, Chairi Nur
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5128

Abstract

Corn (Zea mays L.) is an important agricultural commodity in Indonesia, serving as the second staple food after rice and playing a crucial role in supporting national food security. However, corn production is frequently threatened by sudden outbreaks of pests and diseases, making accurate early detection essential to maintaining yield stability. This study aims to detect maize leaf diseases using five classification algorithms: Support Vector Machine (SVM), Decision Tree, K-Nearest Neighbors (KNN), Random Forest, and Convolutional Neural Network (CNN). These algorithms were tested using a combination of texture and color features, including Gray Level Co-occurrence Matrix (GLCM), Local Binary Pattern (LBP), Hue-Saturation-Value (HSV), and L*a*b*. The dataset used consists of 2,048 maize leaf images classified into four categories: Blight, Common Rust, Gray Leaf Spot, and Healthy, with 512 images per class. Each class was divided into training and testing sets to train and evaluate the classification models. The results show that CNN achieved the highest accuracy of 93.93% when using a complete combination of color and texture features. Meanwhile, SVM also demonstrated high performance, achieving the same accuracy (93.93%) using only the combination of color features (HSV and Lab*). Random Forest and Decision Tree performed best when using color features alone, with accuracies of 89.81% and 87.14%, respectively. These findings indicate that color features have a dominant influence on classification accuracy, and that combining color and texture features can significantly enhance model performance, particularly in CNN architectures. This study contributes to the development of early disease detection systems in precision agriculture.
Prototype Pemilah Buah Stroberi Otomatis menggunakan Kamera berbasis Arduino Uno arifin, nurhikma; Dian Megah Sari; Amalia Chairy
Journal of Computer and Information System ( J-CIS ) Vol 4 No 2 (2021): J-CIS Vol 4 No. 2 Tahun 2021
Publisher : Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31605/jcis.v4i2.1449

Abstract

Perkembangan teknologi yang pesat telah membawa dampak yang besar pada sektor agroindustri. Proses pemilahan buah yang dilakukan secara manual telah mulai digantikan oleh mesin pemilah otomatis. Penelitian ini bertujuan untuk membuat prototype pemilah buah stroberi otomatis menggunakan kamera berbasis arduino uno untuk keperluan otomatisasi industri. Prototype ini menggunakan hardware seperti conveyor belt, box, LED strip, Arduino Uno, Motor Servo SG90 9G Towerpro dan kamera Logitech C920. Data stroberi yang diambil kamera otomatis diolah untuk proses klasifikasi menggunakan algoritma Support Vector Machine (SVM). Arduino Uno akan mengontrol motor servo untuk memilah stroberi berdasarkan hasil klasifikasi algoritma SVM ke dalam 2 jalur, yaitu stroberi yang memenuhi standar dan stroberi yang tidak memenuhi standar. Kategori stroberi yang memenuhi standar adalah stroberi yang sudah matang sedangkan stroberi yang tidak memenuhi standar adalah stroberi belum matang, setengah matang dan busuk. Data yang digunakan terdiri dari 280 data latih dan 80 data uji. Hasil penelitian menunjukkan bahwa alat ini mampu memilah stroberi dengan dengan baik dengan hasil rata-rata akurasi 95%.
Co-Authors Al Farisiy, Salman Amalia Chairy Amirullah, Indrabayu Anaguna, Nursyam Andi M Yusuf Areni, Intan Sari Astinawaty, Astinawaty Chairi Nur Insani Dian Megah Sari Dian Megah Sari Dian Megah Sari Farid Wajidi Fauziah Fauziah Firgiawan, Wawan Heliawaty Hamrul Heri, Adi Iin Indriani Indra Indra Indra Indrabayu Indrabayu Insani, Chairi Nur Intan Sari Areni Ismail Johari, Putri Fausyah Juprianus Rusman Maratuttahirah Maulidia, Ulfa Megah Sari, Dian Milasari Milasari Muh Rafli Rasyid Muhammad Furqan Rasyid Muhammad Furqan Rasyid Musyrifah Muzaki Muzaki Nilam Cahya Nurdina Rasjid Nurfitrianingsih Nurfitrianingsih Nurmadinah, Nurmadinah Prihastinur Rachmini, Siti Aulia Rasyid, Muh. Rafli Sari, Dian Megah Siti Aulia Rachmini Sulfayanti Sulpiana Syam, Nur Aini Upa, Samrius Utama, Muhammad Surya Alif Wawan Firgiawan Yusuf, Andi M