Garuda - Garba Rujukan Digital

Penentuan Takaran Pupuk Nitrogen Tanaman Padi Menggunakan Metode Histogram BWD Sari, Dian Megah; Insani, Chairi Nur; Heri, Adi; Arifin, Nurhikma
Jurnal 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

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.

Integritas Sistem Pembayaran Digital (QRIS) Bagi Pelaku UMKM Husna, Asmaul; Arsyad, Andi Asy'hary J.; Permata, Reny Amalia; Insani, Chairi Nur; Mala, Faiqotul
Bakti Sekawan : Jurnal Pengabdian Masyarakat Vol. 4 No. 2 (2024): Desember
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/bakwan.v4i2.675

To ensure that micro, small, and medium-sized enterprises (MSMEs) in Indonesia implement the Digital Payment System Integrity (QRIS), effective monitoring efforts involving social responsibility are required. The purpose of this dedication to the community is to find out how UMKM perpetrators understand QRIS integration and the problems they face. The implementation methods used include direct support, surveys, transaction monitoring, practical training, and understanding of QRIS and digital payment systems. Therefore, Commitment to the Society provides guidelines for implementing commitment initiatives in the community that focus on QRIS integration for small and medium-sized enterprises. (UMKM). In addition, this PkM will help to expand financial inclusion and inclusive economic growth in Indonesia.

Peningkatan Kompetensi Mahasiswa Dalam Pemrograman Swift Melalui Seminar Dan Sharing Session Interaktif Sari, Dian Megah; Yusuf, Andi M; Musyrifah; Insani, Chairi Nur; Arifin, Nurhikma; Muzaki
Nobel Community Services Journal Vol 5 No 1 (2025): Nobel Community Services Journal
Publisher : Lembaga Penelitian, Publikasi dan Pengabdian Masyarakat ITB Nobel Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37476/ncsj.v5i1.5198

Pesatnya perkembangan teknologi informasi menuntut adanya peningkatan kapasistas sumber daya manusia, terutama dalama penguasaan keterampilan pemograman mobile. Swift, sebagai bahasa utama dalam pengembangan aplikasi iOS, menjadi kemampuan yang penting dikuasai oleh mahasiswa di era digital. Kegiatan ini diselenggarakan dengan tujuan untuk memperkuat pemahaman dan kemampuan mahasiswa terhadap Swift melalui seminar dan sharing session yang bersifat interaktif, melibatkan praktisi dari industry terkait. Metode yang digunakan adalah pendekatan edukatif partisipatif dengan tahapan persiapan, pelaksanaan dan evaluasi. Berdasarkan hasil pre-test dan post-test, terdapat peningkatan signifikan dalam pemahaman mahasiswa, mayoritas peserta yang sebelumnya berada dalam kategori rendah, berpindah ke kategori baik dan sangat baik. Sebanyak 92% peserta menyatakan kegiatan ini membantu mereka memahami Swift secara aplikatif. Observasi juga menunjukkan peningkatan antusiasme mahasiswa dalam diskusi dan praktik langsung. Kegiatan ini menunjukkan bahwa pendekatan interaktif efektif dalam meningkatkan kompetensi mahasiswa dalam pemograman Swift serta mendukung penguatan capaian pembelajran dan kesiapan karier di bidang teknologi mobile.

Classification of Helmet and Vest Usage for Occupational Safety Monitoring using Backpropagation Neural NetworkClassification of Helmet and Vest Usage for Occupational Safety Monitoring using Backpropagation Neural Network Arifin, Nurhikma; Insani, Chairi Nur; Milasari, Milasari; Rusman, Juprianus; Upa, Samrius; Utama, Muhammad Surya Alif
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4781

Occupational Safety and Health (OSH) is a critical aspect in high-risk work environments, where the consistent use of Personal Protective Equipment (PPE) plays a vital role in preventing workplace accidents. However, non-compliance with PPE regulations remains a significant issue, contributing to a high number of work-related injuries in Indonesia. This study proposes an automated detection and classification system for PPE usage, specifically helmets and vests, using the Backpropagation algorithm in artificial neural networks. A total of 100 images were utilized, equally divided between complete and incomplete PPE usage. The dataset was split into 60% training and 40% testing. Image segmentation was performed using HSV color space conversion and thresholding, followed by RGB color feature extraction. The Backpropagation algorithm was then employed for classification. Experimental results show an average accuracy of 90%, with precision, recall, and F-measure all reaching 0.9. Despite some misclassifications due to color similarity between helmets and head coverings, the model demonstrated robust performance with relatively low computational requirements. This study contributes to the field of computer vision and intelligent safety systems by demonstrating the practical effectiveness of lightweight ANN architectures for PPE detection in real-time industrial scenarios, thereby highlighting the potential of backpropagation as an adaptive and practical alternative to more complex deep learning approaches for real-time PPE detection in occupational safety monitoring systems.

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

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.

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

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.

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

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.

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

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.

Analisis Perbandingan Metode Harmonic Mean Filter dan Contraharmonic Mean Filter untuk mengurangi noise pada Citra Digital Chairy, Amalia; Arifin, Nurhikmah; Insani, Chairi Nur
Journal of Computer and Information System ( J-CIS ) Vol 5 No 1 (2022): J-CIS Vol 5 No. 1 Tahun 2022
Publisher : Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31605/jcis.v5i1.1533

The results of digital camera recordings to take digital images, there are often some disturbances that appear in the digital image called noise. The noise that is often found in digital images is Salt-Pepper Noise and Speckle Noise. An image that has noise usually occurs due to an error in image retrieval techniques. Noise that usually appears is spots on the image, it is necessary to reduce noise by using the right filter method so that the resulting image matches the original. One of the filter methods to reduce noise is the harmonic mean-filter and contra-harmonic mean filter methods. From the calculation results of the average Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR), it can be concluded that the Harmonic Mean Filter method is better at reducing salt & pepper noise. While the Contra-Harmonic Mean Filter method is better at reducing speckle noise.

Sistem Pendukung Keputusan Pemilihan Pasangan Hidup Menggunakan Algoritma Analytic Hierarchy Process (AHP) Jean Mewanti Runa; Ismail; Insani, Chairi Nur
Journal of Computer and Information System ( J-CIS ) Vol 6 No 1 (2023): J-CIS Vol 6 No. 1 Tahun 2023
Publisher : Universitas Sulawesi Barat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31605/jcis.v6i1.3590

Abstrak Penelitian ini bertujuan untuk mengembangkan sistem pendukung keputusan pasangan hidup menggunakan algoritma Analytic Hierarchy Process (AHP) berdasarkan kriteria yang dianggap penting oleh masyarakat suku Toraja dalam memilih pasangan hidup. Kriteria yang dipilih meliputi suku, agama, penghasilan, pendidikan, pekerjaan, sifat, dan usia. Metode pengumpulan data dilakukan dengan wawancara kepada responden Tandi Pasau (Ketua adat Mamullu). Hasil penelitian menunjukkan bahwa sistem pendukung keputusan pasangan hidup yang dikembangkan dapat memberikan solusi yang baik dalam membantu proses pemilihan pasangan hidup. Kriteria yang dianggap penting oleh masyarakat suku Toraja dalam memilih pasangan hidup adalah suku, agama, penghasilan, pendidikan, pekerjaan, sifat, dan usia. Berdasarkan perhitungan AHP, karakter menjadi kriteria yang paling dominan dalam memilih pasangan hidup. Sistem pendukung keputusan pasangan hidup menggunakan algoritma AHP yang dikembangkan dapat menjadi alternatif solusi bagi masyarakat suku Toraja dalam memilih pasangan hidup yang tepat. Namun demikian, perlu dilakukan penelitian lebih lanjut untuk menguji keakuratan dan validitas sistem pendukung keputusan ini dengan melibatkan sampel yang lebih besar dan lebih variatif.

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