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All Journal CommIT (Communication & Information Technology) JITK (Jurnal Ilmu Pengetahuan dan Komputer) CSRID (Computer Science Research and Its Development Journal) Building of Informatics, Technology and Science TIN: TERAPAN INFORMATIKA NUSANTARA Syntax: Journal of Software Engineering, Computer Science and Information Technology Jurnal Minfo Polgan (JMP) Jurnal ICT : Information and Communication Technologies
Budi Triandi, Budi
Jurusan Teknik Informatika, STMIK Potensi Utama, Jln. KL. Yos Sudarso Km. 6,5 No. 3-A- Tanjung Mulia, Medan 20241
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IMPELEMENTASI SISTEM PEMANTAUAN OBJEK BERGERAK DENGAN MEMANFAATKAN FREKUENSI RADIO MENGGUNAKAN GPS (GLOBAL POSITIONING SYSTEM) Triandi, Budi
CommIT (Communication and Information Technology) Journal Vol 4, No 1 (2010): CommIT Vol. 4 No. 1 Tahun 2010
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v4i1.533

Abstract

GPS was developed by the United States Department of Defense as a reliable means for accurate navigation. The system provides highly accurate position and velocity information and precise time on a continuous global basis to an unlimited number of properly equipped users. By using combined GPS receiver and microcontroller together with radio system, we can design a monitoring system for our vehicles and display the result on the computer. This system consists of a master module that transmits and receives signals from computer and two slave modules to collect GPS data from vehicles. The result of experiment shows that this system is able to track the vehicle on digital map with accuracy as high as 95%.Keywords: GPS, microcontroller, monitoring, RF
PERANCANGAN DAN IMPLEMENTASI SERVER VOICE OVER INTERNET PROTOKAL (VOIP) DENGAN TRIXBOX PADA WIRELESS LOCAL AREA NETWORK MENGGUNAKAN SMARTPHONE Nahwi, Muhammad Iqbal; Haryanto, Edy Victor; Triandi, Budi
CSRID (Computer Science Research and Its Development Journal) Vol 6, No 2 (2014): CSRID Juni 2014
Publisher : Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (547.866 KB) | DOI: 10.22303/csrid.6.2.2014.87-95

Abstract

ABSTRAK Pada saat ini data telekomunikasi dalam kehidupan manusia sangatlah penting, baik untuk berhubungan antara manusia dalam hal pemenuhan kebutuhan dan informasi maupun hiburan. Sedangkan secara nyata, utuk biaya komunikasi di negara indonesia masih termasuk mahal. Pemanfaatan teknologi VoIP dalam komunikasi yang murah, aman dengan kualitas yang cukup baik sangat tepat untuk diterapkan di negara ini. Pada teknologi VoIP ini berkomunikasi melalui telepon tidak hanya bisa dilakukan dengan mengandalkan pesawat telepon konvensional maupun handphone, namun melalui jaringan internet juga bisa dilakukannya. Dalam penelitian ini metodologi yang digunakan adalah mengimplementasi sistem VoIP pada linux trixbox. Infrastruktur jaringan VoIP yang dirancang menggunakan protokol SIP(session initiation protokol) sebagai protokol komunikasi, memanfaatkan free software linux Trixbox sebagai operating system, asteriks sebagai aplikasi server, dan di sisi client menggunakan softphone X-lite. aplikasi Keyyo VoIP digunakan untuk client pada smartphone.
Wireless Sensor Network Sebagai Penentu Lokasi Kebakaran Hutan Rahmad, Iwan Fitrianto; Yusfrizal, Yusfrizal; Tanti, Lili; Triandi, Budi
TIN: Terapan Informatika Nusantara Vol 2 No 3 (2021): Agustus 2021
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

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Abstract

Forests have an important role for living things today. Where the forest is the main source of oxygen for living things. Based on data from the Direktorat Jenderal Planologi Kehutanan dan Tata Lingkungan (PKTL) of the Ministry of Environment and Forestry, it shows that the total forest area of ​​the entire landmass of Indonesia is 94.1 million hectares or 50.1% of the total area. area. land, and about 274,375 hectares burned. In overcoming this problem, the government has taken various ways, ranging from advocating and imposing legal sanctions on individuals and groups who threaten forest sustainability. In the case of forest fires, they are often detected early and get help from the fire department. So the purpose of this research is to carry out early warning of forest fires using NodeMCU. With the help of NodeMCU will provide fast information to find out fires early so that fires can be contained before they spread widely. This technology will provide information on where the fire points are and send that information through the network to the fire monitoring application that is made. To determine location accuracy, the Mamdani fuzzy method is one part of the Fuzzy Inference System which is useful for drawing or making the best decisions in uncertain problems. will be an innovative medium for any fire information in an area so that it quickly and instantly knows the location of the hotspots and directly occurs at the hotspots so that the burning effect is extensive and results in large losses as well as the effects of air pollution that cause large fires
RANCANG BANGUN APLIKASI PREDIKSI PENJUALAN ATK PADA KIOS TINTA BERBASIS ANDROID MENGGUNAKAN METODE LEAST SQUARES Riswanto, Riswanto; Triandi, Budi
Syntax : Journal of Software Engineering, Computer Science and Information Technology Vol 5, No 2 (2024): Desember 2024
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/syntax.v5i2.5506

Abstract

Kios Tinta menerima penjualan dalam jumlah besar dan jumlah kecil. Namun ada beberapa kendala yang dihadapi oleh Kios Tinta, yaitu sering terjadi proses pemenuhan permintaan dari konsumen tidak dapat dipenuhi secara maksimal, serta pendataan stok produk untuk di jual kembali masih di periksa oleh bagian gudang secara terus menerus.  Permasalahan lainnya yang dihadapi Kios Tinta adalah belum adanya sistem yang dapat melakukan proses Prediksi penjualan produk secara cepat dan tepat sehingga tidak terjadi kelebihan atau kekurangan persediaan dan proses penjualan dapat berjalan lancar. Dalam hal ini di lakukan untuk mendata ketersedian produk perbulannya, agar tidak terjadi keterlambatan dalam penambahan atau persedian stok produk untuk selanjutnya. Kios Tinta mengalami kendala dalam menentukan prediksi jumlah stok produk di waktu yang akan datang, stok produk pada Kios Tinta sering tidak stabil, sering terjadi kekurangan stok sehingga tidak dapat memenuhi permintaan dari konsumen. Dalam rangka meningkatkan mekanisme proses penjualan produk, Kios Tinta membutuhkan aplikasi untuk menentukan jumlah penjualan pada periode yang akan datang. Kios Tinta sering terjadi kekurangan atau penumpukan jumlah produk yang akan dijual serta tidak sesuai dengan jumlah permintaan dari pelanggan. penulis akan merancang sebuah sistem prediksi penjualan produk dengan metode Least Squares, dimana metode ini merupakan Metode Least Square merupakan salah satu metode berupa data deret berkala atau time series, yang mana dibutuhkan data-data penjualan dimasa lampau untuk melakukan Prediksi penjualan dimasa mendatang sehingga dapat ditentukan hasilnya Kata Kunci— Prediksi, Penjualan, Android Studio, Mysql ABSTRACT Ink Kiosk accepts sales in large quantities and small quantities. However, there are several obstacles faced by the Ink Kiosk, namely that it often happens that the process of fulfilling requests from consumers cannot be fulfilled optimally, and the data collection on product stock for resale is still being checked by the warehouse department continuously.  Another problem faced by Kiosk Ink is that there is no system that can carry out the product sales forecasting process quickly and accurately so that there is no excess or shortage of inventory and the sales process can run smoothly. In this case, this is done to record monthly product availability, so that there are no delays in adding or supplying product stock for the next time. Ink Kiosk experiences problems in determining predictions of product stock amounts in the future, product stock at Ink Kiosk is often unstable, stock shortages often occur so they cannot meet consumer demand. In order to improve the product sales process mechanism, Kios Ink needs an application to determine the number of sales in the coming period. Ink Kiosks often experience shortages or accumulations of the number of products to be sold and do not match the number of requests from customers. The author will design a product sales prediction system using the Least Squares method, where this method is the Least Squares Method which is a method in the form of periodic series or time series data, where past sales data is needed to forecast future sales so that it can be determined the resultKeywords—Prediction, Sales, Android Studio, Mysql  
Designing an Online Train Ticket Booking Website Using the Design Thinking Method Cania, Dea; Triandi, Budi
Jurnal ICT : Information and Communication Technologies Vol. 16 No. 2 (2025): October, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jict.v16i2.282

Abstract

The increasing demand for train services in Indonesia has highlighted significant challenges in conventional ticket reservation systems, including long queues, limited customer time, scheduling errors, and rapid ticket depletion during peak periods. This study aims to develop and evaluate a digital train ticket reservation application designed to streamline booking processes, improve user experience, and provide real-time access to ticket availability, pricing, and schedules. The research employs a user-centered Design Thinking approach to guide the development of key functional modules, including login, train and route management, schedule creation, order processing, and user profile management. Results demonstrate that the application successfully integrates these functionalities into a unified platform, reducing operational inefficiencies, minimizing user errors, and enabling users to book tickets conveniently from multiple devices. The system enhances administrative oversight through centralized order reporting and data management, while improving overall service reliability and user satisfaction. These findings imply that human-centered digital platforms can transform traditional public transportation services by increasing operational efficiency, scalability, and customer convenience. Furthermore, the study identifies areas for future improvement, such as incorporating predictive analytics for demand forecasting, dynamic seat allocation, and extended accessibility features to accommodate diverse user needs. The results provide practical insights for railway operators seeking to implement adaptive, reliable, and user-friendly ticket reservation systems, underscoring the value of integrating technology, usability, and real-time information in public transport services.
System Application Design Inventory Management in Sales Using Genetic Algorithms Siahaan, Reinhard Parningotan; Triandi, Budi
Jurnal ICT : Information and Communication Technologies Vol. 16 No. 2 (2025): October, Jurnal ICT : Information and Communication Technologies
Publisher : Marqcha Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/jict.v16i2.259

Abstract

PT. Kao Indonesia, a company engaged in the production and distribution of consumer goods, requires an efficient inventory management system to ensure a smooth and responsive sales process. One of the main challenges faced is the discrepancy between stock levels and market demand, which often leads to overstocking or stockouts and ultimately financial losses. This study aims to design and develop an inventory management system application that optimizes stock levels using a Genetic Algorithm (GA). The GA method is employed to determine the optimal inventory quantity by analyzing historical sales data and evaluating various stock-level scenarios to find the most efficient solution. The application was developed using the PHP programming language and a MySQL database. A case study at PT. Kao Indonesia involving sales and product inventory data over a specific period demonstrates that the system effectively enhances stock management efficiency, minimizes inventory discrepancies, and supports more accurate and data-driven decision-making in the company’s inventory management process.
OPTIMIZATION OF MLP-NN FOR MANGO LEAF DISEASE PREDICTION USING IMAGE-BASED FEATURE EXTRACTION Triandi, Budi; Tanti, Lili
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7031

Abstract

Mango (Mangifera indica Linn.) is a nutrient-rich fruit, yet leaf diseases caused by microorganisms can significantly reduce crop productivity. Early detection is essential to prevent further damage and support effective disease management. This study proposes an optimized mango leaf disease prediction model using a multi-layer perceptron neural network (MLP-NN). Image-based feature extraction is performed using the Inception v3 architecture to obtain high-level color and texture features that improve classification performance. Unlike previous studies that rely solely on manually engineered features or full CNN training, this research introduces a hybrid approach that integrates deep feature extraction with MLP-NN optimization, offering a lightweight yet highly accurate alternative. Several hyperparameter combinations, including activation functions (ReLU, tanh, sigmoid) and optimization algorithms (Adam and SGD), were evaluated using the Orange platform. The optimized MLP-NN model with ReLU and Adam achieved the highest accuracy of 93.5%, demonstrating better stability and training efficiency compared to other configurations. These findings highlight the novelty and advantages of the proposed method, showing improved accuracy with lower computational cost relative to many existing approaches. This study provides an efficient solution for mango leaf disease prediction and supports future development of automated plant disease detection systems
Comparative Analysis of K-NN and Naïve Bayes Algorithms for Early-Stage Chronic Kidney Disease Classification Rahma, Intan Dwi; Furqan, Mhd; Triandi, Budi
Building of Informatics, Technology and Science (BITS) Vol 7 No 4 (2026): March 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i4.9392

Abstract

Chronic Kidney Disease (CKD) is a global health issue characterized by low early detection rates and high diagnostic costs. Artificial intelligence, particularly machine learning, offers a promising solution as a rapid and cost-effective decision support system. This study aims to comprehensively analyze and compare the performance of two simple and interpretable classification algorithms, K-Nearest Neighbor (K-NN) and Naïve Bayes (NB), for predicting CKD based on clinical data. The dataset was sourced from the UCI Machine Learning Repository, comprising 400 instances and 25 clinical attributes such as blood pressure and serum creatinine. The methodology included data preprocessing (median imputation for numerical features, mode imputation for categorical features), encoding, Min-Max normalization, data splitting (70:30 ratio), model training, K parameter optimization for K-NN via 5-fold cross-validation, and evaluation using accuracy, precision, recall, F1-Score, and Confusion Matrix metrics. Experimental results demonstrated that the Naïve Bayes algorithm achieved superior performance with an accuracy of 95.83%, precision of 95.95%, recall of 97.26%, and F1-Score of 96.60%. The K-NN algorithm with an optimal K=5 attained an accuracy of 91.67%. Statistical analysis using a paired t-test (α=0.05) with p-value=0.012 confirmed that this performance difference was significant. It is concluded that Naïve Bayes is more effective for this CKD dataset, likely due to its robustness in handling feature independence assumptions and varied data scales. This model holds strong potential for development into an early-stage CKD screening tool to assist healthcare professionals.
Optimasi Support Vector Machine Menggunakan Particle Swarm Optimization pada Analisis Sentimen Program Efisiensi Anggaran Pemerintah Nurhayati, Nurhayati; Tanti, Lili; Triandi, Budi
Jurnal Minfo Polgan Vol. 15 No. 1 (2026): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v15i1.15929

Abstract

Respon publik terkait kebijakan pemerintah, termasuk program efisien anggaran dapat dipahami menggunakan pendekatan analisis sentimen. Penelitian ini bertujuan membandingkan kinerja algoritma Support Vector Machine (SVM) dengan kernel Radial Basis Functional (RBF), kernel Linear, dan kernel Polynomial, serta mengevaluasi pengaruh Particle Swarm Optimization (PSO) terhadap peningkatan performa klasifikasi. Dataset sebanyak 4274 data diperoleh melalui teknik crawling dari media sosial X (Twitter) dan kemudian diproses melalui langkah-langkah pembersihan teks seperti cleaning, case folding, normalization, tokenization, stopword removal, stemming, labeling menggunakan lexicon based serta menggunakan TF-IDF (Term Frequency-Inverse Document Frequency) untuk ekstraksi fitur. Proses penilaian kinerja model dilaksanakan dengan memanfaatkan indikator accuracy, precision, recall, dan F1-score, serta didukung oleh analisis confusion matrix. Berdasarkan hasil pengujian yang diperoleh, penelitian ini menunjukkan bahwa SVM kernel Linear berhasil meningkatkan akurasi menjadi 0.7579 atau 75.79%, sedangkan pada kernel RBF dan Polynomial tidak memberikan peningkatan signifikan. Selain itu, kelas netral menjadi kelas yang paling sulit diklasifikasikan. Penelitian ini menyimpulkan bahwa kombinasi SVM Linear dan PSO merupakan model terbaik untuk analisis sentimen kebijakan efisiensi anggaran pemerintah, serta menegaskan pentingnya pemilihan kernel dan strategi optimasi yang tepat dalam pengembangan sistem klasifikasi berbasis machine learning.
Co-Authors Cania, Dea Edy Victor Haryanto, Edy Victor Iwan Fitrianto Rahmad Lili Tanti, Lili Mhd Furqan Muhammad Iqbal Nahwi, Muhammad Iqbal Nurhayati Nurhayati Pairin, Yusfrizal Bin Rahma, Intan Dwi riswanto riswanto Siahaan, Reinhard Parningotan