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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan JOIN (Jurnal Online Informatika) J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Jurnal Mantik Indonesian Journal of Electrical Engineering and Computer Science Jurnal ComunitA Servizio
Rudy Chandra
Institut Teknologi Del
Humanities Computer Science & IT Economics, Econometrics & Finance Education Engineering Languange, Linguistic, Communication & Media Library & Information Science Nursing Social Sciences

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Identifikasi Penyakit Diabetic Retinopathy menggunakan Learning Vector Quantization (LVQ) Rudy Chandra; Erna Budhiarti Nababan; Sawaluddin Sawaluddin
InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Vol 6, No 1 (2021): InfoTekJar September
Publisher : Universitas Islam Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30743/infotekjar.v6i1.3913

Abstract

Diabetic retinopathy (retinopati diabetik) merupakan sejenis penyakit mata yang terjadi pada pengidap diabetes. Untuk mendeteksi jenis penyakit ini, dokter mata biasanya akan melakukan pemeriksaan dengan cara memeriksa mata dengan pupil lebar dan komprehensif. Adapun hambatan dalam mendeteksi retinopati diabetik adalah alat pemeriksaan yang belum masif dan belum memadai serta masih memakan waktu dalam mengidentifikasi tahap demi tahap pada retina manual. Berdasarkan masalah tersebut dibutuhkanlah suatu sistem untuk membantu dokter dalam mengidentifikasi retina yaitu dengan menerapkan pattern recognition menggunakan algoritma Learning Vector Quantization (LVQ). Sistem yang dijalankan dengan memasukkan citra tetina kemudian akan melaui proses preprocessing citra dan ekstraksi fitur statistik untuk mendapatkan hasil yang sesuai untuk dilakukan identifikasi menggunakan LVQ. Data retina yang digunakan terbagi menjadi 3 yaitu data training, data validation dan data testing. Pada data validation diuji dan mendapatkan hyperparameter untuk membentuk model jaringan terbaik yaitu pada epoch 50 dan learning rate 0,001. Kemudian dilakukan pelatihan hingga menghasilkan bobot akhir dengan algoritma pelatihan LVQ. Bobot akhir tersebut akan digunakan pada proses pengujian dengan data uji dan menghasilkan accuracy 82% sensitivity 80% dan precision 83,33%
Wood Classification For Efficiency in Preventing Illegal Logging Using K-Nearest Neighbor Rudy Chandra
Jurnal Mantik Vol. 6 No. 1 (2022): May: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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

Abstract

Wood is a commodity that is usually used for various purposes. Growing demands for timber have led some humans to commit fraudulent and unlawful moves. One of them is through illegal logging of wood species that are protected by the state. The Ministry of Environment and Forestry apart from forest rangers has several problems in classifying wood species. Then technology is used to overcome these problems through the use of machine learning. One suitable algorithm for classifying is K-NN. There are five types of information wood used, specifically marine resin, teak, kruing, meranti, and ironwood. The total wood photos are 1,300 with a complete dataset of 250 images for each type of wood and 10 images for classification testing. The trial was carried out by finding the most optimal K, ie K = 3, after that it was recorded with a confusion matrix. The results obtained are 76% accuracy, 78.8% recall, 76% precision, and 77.37% F1-Score. The higher the value of K, the greater the number of classifications and the lower the accuracy. The higher the value of K, the more types of classifications you want to test, and the less accurate the percentages in the classification process.
IMPLEMENTATION OF BRUTE-FORCE ALGORITHM AND BACKTRACKING ALGORITHM FOR FIREFIGHTING ROBOT SIMULATION Tegar Arifin Prasetyo; Rudy Chandra; Wesly Mailander Siagian; Tahan HJ Sihombing; Sarbaini Sarbaini
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 10, No 1 (2023)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v10i1.456

Abstract

In general, a robot is defined as a mechanical device used by humans to ease human work. Robots are usually used for difficult and dangerous tasks. One example of its use is a firefighting robot that replaces human tasks in extinguishing fires. The firefighting robot is on duty to find fire spots in a city then extinguishing them. To be able to put out a fire, the robot must implement an efficient program in finding and determining the shortest path to the location of the fire and then put it out. For this reason, the robot is equipped with proximity and fire sensors to detect the presence of fire. The design is made with a three-step program that is designing needs of robot control, robot control mechanism scheme preparation and implementing an algorithm for making program syntax. The Brute-Force Algorithm can be implemented to indicate the presence of a hotspot signal and the backtracking Algorithm is implemented to find the shortest path to the hotspot location. This paper discusses the use of a brute-force algorithm and a backtracking algorithm in a firefighting robot program to make the fire search process more efficient. The results show that from 8 input fire points, the firefighting robot is able to find all the points within 3.12 seconds with 13 times trial. In its application, the writer used Visual Basic 6.0 in the firefighting robot program.Keywords: Firefighting Robot, Brute-Force Algorithm, and Backtracking Algorithm.Secara umum robot didefinisikan sebagai suatu alat mekanik yang digunakan oleh manusia untuk mempermudah pekerjaan manusia. Robot biasanya digunakan untuk tugas-tugas yang sulit dan berbahaya. Salah satu contoh penggunaannya adalah robot pemadam kebakaran yang menggantikan tugas manusia dalam memadamkan api. Robot pemadam kebakaran bertugas untuk menemukan titik api di suatu kota kemudian memadamkannya. Untuk dapat memadamkan api, robot harus menerapkan program yang efisien dalam mencari dan menentukan jalur terpendek menuju lokasi kebakaran kemudian memadamkannya. Untuk itu, robot dilengkapi dengan proximity dan fire sensor untuk mendeteksi adanya api. Perancangan dibuat dengan tiga langkah program yaitu perancangan kebutuhan pengendalian robot, penyusunan skema mekanisme kendali robot dan implementasi algoritma untuk pembuatan sintaks program. Algoritma Brute-Force dapat diimplementasikan untuk menunjukkan adanya sinyal hotspot dan Algoritma backtracking diimplementasikan untuk mencari jalur terpendek ke lokasi hotspot. Penelitian ini membahas tentang penggunaan algoritma brute force dan algoritma backtracking pada simulasi program robot pemadam kebakaran agar proses pencarian kebakaran menjadi lebih efisien. Hasil penelitian menunjukkan bahwa dari 8 input titik api, robot pemadam kebakaran mampu menemukan semua titik dalam waktu 3,12 detik dengan 13 percobaan. Dalam penerapannya penulis menggunakan Visual Basic 6.0 pada program robot pemadam kebakaran. Kata kunci: Robot Pemadam Kebakaran, Algoritma Brute-Force, dan Backtracking.
Sales forecasting of marketing using adaptive response rate single exponential smoothing algorithm Tegar Arifin Prasetyo; Evan Richardo Sianipar; Poibe Leny Naomi; Ester Saulina Hutabarat; Rudy Chandra; Wesly Mailander Siagian; Goklas Henry Agus Panjaitan
Indonesian Journal of Electrical Engineering and Computer Science Vol 31, No 1: July 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v31.i1.pp423-432

Abstract

Micro, small and medium enterprises (UMKM) is one of the important aspects to support the improvement of the economy in Indonesia. Zee Mart’s business is one of the UMKM shop in Pematang Siantar City with sales and purchase transaction activities for supplies. The purpose of this study is to predict the sales of Zee Mart store goods in the coming month using the adaptive response rate single exponential smoothing (ARRSES) method. ARRSES is a method with the advantage of having two parameters, alpha and beta, where alpha will change every period when the data pattern changes. The dataset obtained will be pre-processed through data selection, cleaning, and transformation. The best beta is determined based on the level of accuracy calculated using the mean absolute percentage error (MAPE). Model development using the ARRSES method will produce forecasting percentages and errors for each product using MAPE. The number of sales data is 23,092 before preprocessing and 23,021 after pre-processing, with the total quantity of goods sold being 149,764 of 1,492 products. The results obtained using sales data 23,021 show the lowest MAPE value of 9.85 at the best beta of 0.6 with the highest accuracy of 90.15% and the model is implemented into a web interface.
SALARY PREDICTION OF IT EMPLOYEES IN JAVA USING LINEAR REGRESSION ALGORITHM Rudy Chandra; Tegar Arifin Prasetyo; Sarbaini Sarbaini
KLIK- KUMPULAN JURNAL ILMU KOMPUTER Vol 10, No 2 (2023)
Publisher : Lambung Mangkurat University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/klik.v10i2.635

Abstract

The payroll system is very influential on a company's workers' welfare in achieving company goals. Appropriate payroll will build morale for the workforce so that they can advance the company through the work ethic and professionalism of the crew. The salary calculation system for employees must be adjusted to several criteria, such as their city and job role. Long experience can also be used as a calculation criterion in providing salary. For this reason, an approach is needed to provide a decent and good salary prediction for the company's consideration. One of the models commonly used in making predictions is linear regression. Linear regression is a model that calculates the relationship between two variables with independent values and bound data. This research develops a system by implementing a Linear Regression algorithm to predict the salaries of IT employees in Java. The model that has been created is then built using the Python language and implemented into a website-based visualization form that is easy to understand with Streamlit. The modeling results were tested and gave an MSE value of 8240258.48. This research is expected to be a reference in research related to this topic in the future and can be used by companies that have difficulties in determining employee salaries
Pembuatan Website Kelompok Maduma Tani Asido Saragih; Samuel Sibuea; Fritz Marpaung; Lawy Xenna; Tegar Arifin; Rudy Chandra; Asido Saragih
JURNAL Comunità Servizio : Jurnal Terkait Kegiatan Pengabdian kepada Masyarakat, terkhusus bidang Teknologi, Kewirausahaan dan Sosial Kemasyarakatan Vol. 5 No. 2 (2023): OKTOBER
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LPPM), Univesitas Kristen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33541/cs.v5i2.5010

Abstract

Sipituhuta Village is one of the villages in Pollung District which has commodities in the agricultural sector. In 2016 a farmer group called the Maduma Tani Group was established as a means for sustainable agriculture to increase income. This farmer group provides farming tools that can be borrowed by farmers. However, the lack of information about the availability of farming tools and education on agricultural processing causes the process of borrowing tools and seeking agricultural processing education is still manual. The use of the website is one of the solutions in the field of information technology that helps increase farmers' knowledge in solving agricultural problems they face and borrowing farming tools in Sipituhuta Village. The development of this website uses the prototyping method, where this method is very suitable for building small-scale and customized websites that are created based on certain requests and needs. Website development using the PHP programming language, Laravel framework, and MySQL Database Management System (DBMS). Some of the features that have been successfully developed include lending tools, viewing history of tool lending, viewing education, viewing farming projects, viewing notifications. The result of this activity is that farmers in Sipituhuta Village can easily find information about agriculture and borrow farming tools. Website testing is done using the black box method. The test results show that the website that was built has been successfully running according to its function.
Evaluating the efficacy of univariate LSTM approach for COVID-19 data prediction in Indonesia Tegar Arifin Prasetyo; Joshua Pratama Silitonga; Matthew Alfredo; Risky Saputra Siahaan; Roberd Saragih; Dewi Handayani; Rudy Chandra
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1353-1366

Abstract

The coronavirus disease 2019 (COVID-19) pandemic, originating in 2020, has emerged as a critical global issue due to its rapid and widespread transmission. Indonesia, among the affected nations, has taken measures to address the situation, including the development of a deep learning model for predicting future COVID-19 infection and spread. This predictive tool serves as a valuable reference for the government and stakeholders, aiding them in making informed decisions and implementing appropriate measures to contain the virus. The deep learning model employs the long short-term memory (LSTM) algorithm, chosen for its ability to recognize temporal patterns in the country’s COVID-19 data. The model creation process involves data collection, preprocessing, model architecture planning, modeling, training, and evaluation. Two LSTM models were developed: a univariate and a multivariate model. Following thorough training and evaluation, the univariate model emerged as the superior choice, boasting evaluation metrics of 16.72 for mean absolute percentage error (MAPE) and 66.36 for root mean squared error (RMSE). This model was then deployed on a publicly accessible website, presenting visualizations of past COVID-19 data and predictions of future cases through line graphs. This user-friendly platform enables the public to access and analyze the data easily.
Refining tomato disease recognition: hyperparameter tuning on ResNet-101 architecture for precise leaf-based classification Tegar Arifin Prasetyo; Tiurma Lumban Gaol; Nico Felix Sipahutar; Tessalonika Siahaan; Trito Exaudi Manik; Rudy Chandra
Indonesian Journal of Electrical Engineering and Computer Science Vol 34, No 2: May 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v34.i2.pp1204-1213

Abstract

Tomatoes plants are widely recognized as versatile vegetables globally. This study aims to develop a high-precision web interface for classifying various leaf diseases in tomatoes. Utilizing a convolutional neural network (CNN) algorithm using the residual network-101 (ResNet-101) architecture, this tool aids farmers in accurately identifying leaf diseases in tomatoes, thereby reducing the risk of crop failure. The dataset comprises 6,800 images, categorized into four classes: early blight, spider mites two spotted, tomato yellow leaf curl virus, and healthy tomatoes, each containing 1,700 images. Hyperparameter tuning was conducted as part of an experiment aimed at enhancing the performance of the model. Experiments involved varying epoch values (10, 25, 30, 50, 60, 75, 100, and 110), a fixed batch size of 4, different learning rates (0.1, 0.01, 0.001, 0.0001), and num workers (4, 8, 16). The results demonstrated an accuracy of 99% with 100 epochs, a batch size of 4, a learning rate of 0.001, and 16 num workers. Consequently, this research contributes to a deeper understanding of disease management in tomato plants, ensuring optimal quality and quantity of the harvest.
Penerapan Website untuk Digitalisasi dan Pengembangan Bisnis di Usaha Pemandian Air Panas Karunia Sipoholon Rudy Chandra; Tegar Arifin Prasetyo; Tahan HJ Sihombing; Juan Carlos Munthe; Christian Benedict Lumbantoruan; Dame Sisri Haryati Katarina Rumapea; Herbeth Augustinus Napitupulu
JURNAL Comunità Servizio : Jurnal Terkait Kegiatan Pengabdian kepada Masyarakat, terkhusus bidang Teknologi, Kewirausahaan dan Sosial Kemasyarakatan Vol. 6 No. 1 (2024): APRIL
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat (LPPM), Univesitas Kristen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33541/cs.v6i1.5297

Abstract

Traveling is an activity that people do to unwind from their daily routines. There are many types of travel destinations, and one of them is a natural hot spring located in Situmeang Habinsaran Village, Sipoholon District, North Tapanuli. Besides being a tourist destination, natural hot springs are believed to have the ability to cure various skin diseases and promote healthier skin. The owner of the Karunia hot spring business is one of the entrepreneurs who provide hot spring baths, accommodations/homestays, and a restaurant. This business opportunity has become more competitive with many similar ventures opening up. Marketing and promoting this natural attraction continues. As time goes by, the promotion and business systems need to be enhanced by using information technology to improve. A website is one of the means for promotion and branding, enabling the digitization of the established business. Building a website is applied for promotional purposes and to enhance credibility with the public, making them more familiar with the hot spring tourism in Sipoholon, especially Karunia hot spring. The website also serves as a centralized platform for financial management, cash flow, and business operations, which have previously been done manually. The goal of creating this website is for partners to increase income and improve business branding
Development of a Mobile-Based Application for Classifying Caladium Plants Using the CNN Algorithm Rudy Chandra; Tegar Arifin Prasetyo; Heni Ernita Lumbangaol; Veny Siahaan; Johan Immanuel Sianipar
JOIN (Jurnal Online Informatika) Vol 9 No 1 (2024)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v9i1.1296

Abstract

Caladium is a popular ornamental plant and has business potential. However, difficulties in recognizing the type of Caladium often occur because of the similarities in shape, pattern, and color of the leaves between the different kinds of Caladium. To overcome this problem, research will use machine learning with the Convolutional Neural Network (CNN) algorithm to build a mobile application that can accurately classify four types of Caladiums. The data set used is 1200 data with four classes; each class has 300 data. The best model is found with the parameter epoch 100, learning rate 0.001, and batch size 64. The model is then implemented in a mobile application with two menus, "Take a photo" and "Choose an image," which will display the classification output and confidence values of the four types of Caladiums. Testing with 30 test data per class achieves 0.975 accuracy on both menus. On the “Take a photo” menu, precision is 0.974, recall is 0.9725, and f1-score is 0.965. Meanwhile, on the “Choose an image” menu a precision and recall value is 0.975, and f1-score value of 0.97.
Co-Authors Anastasia Marsada Uli Simamora Asido Saragih Christian Benedict Lumbantoruan Dame Sisri Haryati Katarina Rumapea Dewi Handayani Emy Sonia Sinambela Erna Budhiarti Nababan Ester Saulina Hutabarat Evan Richardo Sianipar Frengki Simatupang Fritz Marpaung Goklas Henry Agus Panjaitan Heni Ernita Lumbangaol Henry Agus Panjaitan , Goklas Henry Agus Panjaitan, Goklas Herbeth Augustinus Napitupulu Italiano Wowiling, Gerry Johan Immanuel Sianipar Joshua Pratama Silitonga Juan Carlos Munthe Lawy Xenna Lumban Gaol, Tiurma Matthew Alfredo Mula Timbul Sigiro, Marojahan Nico Felix Sipahutar Panca Rahmat Siagian, Iqbal Pangaribuan, Maria Partogi Pardede, Immanuel Pasaribu, Monalisa Poibe Leny Naomi Prasetyo, Tegar Risky Saputra Siahaan Roberd Saragih Samuel Sibuea Sarbaini Sarbaini Sawaluddin Nasution SIAGIAN, WESLY MAILANDER Suandika Napitupulu Tahan HJ Sihombing Tessalonika Siahaan Tiurma Lumban Gaol Trito Exaudi Manik Veny Siahaan