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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI)
Christnatalis
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Computer Science & IT

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IMPLEMENTATITON OF RANDOM FOREST ALGORTIHM ON SALES DATA TO PREDICT CHURN POTENTIAL IN SUZUYA SUPERMARKET PRODUCTS Dharma, Abdi; Christnatalis; Candra, Windy; Turnip, Josua Presen
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 2 (2023): Research Article, Volume 7 Issue 2 April, 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.12243

Abstract

Concentration of sales that are focused on products that are in great demand and are popular is one of the supermarket sales techniques. Seasonal sales techniques like this sometimes have an impact that can be seen obviously by the imbalance in sales of existing products in supermarkets. Sales imbalance can be the initial cause for a product to lose interest and become a product that is eventually removed from store. With a classification model made to predict which products will be eliminated or churn, it can assist staff in distributing the sales of each product. The more products are churn due to lack of enthusiasts which can affect the overall sales of the supermarket. The purpose of this study is to assist staff in classifying potentially churn products. The classification model consists of 3 models with different algorithms and the results show that the application of the Random Forest algorithm is more effective for predicting data with 96% accuracy compared to 81% for the Logistic Regression algorithm and 46% for the Support Vector Machine algorithm.
Smart Diagnosis of Coffee Diseases via Web-Based Expert System Ginting, Deo Ekel Pindonta; Sitorus Pane, Siti Anzani; Nababan, Marlince Novita Karoseri; Christnatalis
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 3 (2025): Article Research July 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i3.14974

Abstract

Indonesia’s coffee industry faces persistent threats from plant diseases and pests, which significantly impact crop yield and farmer livelihoods. Many smallholder farmers lack access to timely expert guidance, leading to delays in diagnosis and ineffective treatments. This study proposes a web-based expert system designed to assist farmers in diagnosing coffee plant diseases and pests based on observed symptoms. The system integrates a Bayesian Network (BN) to model the probabilistic relationships between symptoms and diseases. It employs a Breadth-First Search (BFS) algorithm to optimize the exploration of symptom-disease associations. Developed using Node.js, Next.js, and MySQL, the system enables users to input their symptoms and receive probabilistic diagnoses along with treatment suggestions. Validation results show over 85% accuracy compared to expert assessments, highlighting the system's reliability and scalability. This research demonstrates that combining probabilistic reasoning and structured graph traversal provides an effective diagnostic tool, especially for underserved rural communities. Furthermore, the system serves as a foundation for future development of intelligent agricultural support tools, with potential integration of real-time environmental data, mobile platforms, and adaptive learning models to enhance decision-making in precision farming.
Perbandingan Algoritma Adaptive Approximated Median Filtering dan algoritma Nonsubsampled Contourlet Transform dalam pengurangan derau (noise) citra digital Christnatalis; Steven; Aswani; Imam Mulqan, MHD Awal; Salim, Andy
Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Vol. 3 No. 1 (2020): Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI)
Publisher : Utility Project Solution

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9767/jikomsi.v3i1.78

Abstract

Noise pada citra tidak hanya terjadi karena ketidaksempurnaan dalam proses pengambilan gambar, tetapi bisa juga disebabkan oleh kotoran-kotoran yang terjadi pada citra. Dalam tugas akhir ini noise yang digunakan adalah impulse noise (salt and pepper). Impulse noise biasanya terjadi selama transisi citra. Noise ini tampak sebagai impuls-impuls hitam atau putih diatas citra. Impulse noise ini dapat terjadi karena error bit acak pada saluran komunikasi. Dalam literatur, dapat ditemukan berbagai algoritma yang dapat digunakan untuk menghilangkan salt and pepper noise, seperti Adaptive Approximated Median Filter dan Non-Subsampled Contourlet Transform (NSCT). Perangkat lunak yang dirancang ini melakukan beberapa tahapan proses yaitu dimulai dari proses menentukan sebuah piksel adalah noise atau tidak berdasarkan pada nilai threshold yang diberikan. Apabila piksel tersebut merupakan noise maka nilai baru akan dihitung dan di-set pada tahapan noise reduction. Terakhir, fase image enhancement akan dilakukan untuk menghasilkan citra digital dengan kualitas yang lebih bagus. Perangkat lunak ini akan menyisipkan noise ke dalam citra input. Setelah itu, noise tersebut akan dihapus dengan menggunakan metode Adaptive Approximated Median Filter dan Non-Subsampled Contourlet Transform (NSCT). Terakhir, akan dilakukan pengujian terhadap citra hasil reduksi noise dengan menggunakan metode MSE dan PSNR.
Co-Authors Abdi Dharma Aswani Candra, Windy Ginting, Deo Ekel Pindonta Imam Mulqan, MHD Awal Nababan, Marlince Novita Karoseri Salim, Andy Sitorus Pane, Siti Anzani Steven Turnip, Josua Presen