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All Journal Sinkron : Jurnal dan Penelitian Teknik Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA Dinamisia: Jurnal Pengabdian Kepada Masyarakat JURTEKSI Jurnal Mantik IICS JME
Arie Satia Dharma
Institut Teknologi Del
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Social Sciences Other

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The User Personalization with KNN for Recommender System Dharma, Arie Satia
Sinkron : jurnal dan penelitian teknik informatika Vol. 3 No. 2 (2019): SinkrOn Volume 3 Number 2, April 2019
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (371.142 KB) | DOI: 10.33395/sinkron.v3i2.10047

Abstract

Following the increase in of the information available on the Web, it is important to diversity of its users and the complexity of Web applications. One web application that has a diversity of users is a news website. Customizing a website with the characteristics of each user is called personalization. The purpose of this study is to study the methods used in giving news recommendations using user personalization. Collaborative filtering method (CF) is one method that groups users based on the nature of the user. This CF method can be applied using the k-nearest neighbor (KNN) algorithm. The proximity between users in this algorithm is sought using the Pearson correlation technique and cosine correlation. The best technique by considering the smallest value of prediction error evaluation will be applied to giving recommendations. Evaluation of these errors was tested by applying the formula Root Mean Square Error. The best evaluation results obtained in this study are the k-nearest neighbor algorithm with cosine correlation similiarity.
Prediksi Stok Obat di RSU HKBP Balige Menggunakan Adaptive Neuro-Fuzzy Inference System Dharma, Arie Satia; Tampubolon, Lily Andayani; Purba, Daniel Somanta
Sinkron : jurnal dan penelitian teknik informatika Vol. 5 No. 1 (2020): Article Research, October 2020
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v5i1.10529

Abstract

Currently the purchases of drugs at Instalasi Farmasi RSU (IFRS) HKBP Balige are based on the examination of the amount of drugs usage. The purchases of drugs based on the examination of the amount of drugs usage cause frequent unplanned drugs purchases that must be hastened (cito) and purchases to other pharmacies. The purchases of cito and purchases to other pharmacies will inflict a financial loss to the patients, because when IFRS makes drugs purchases of cito or to other pharmacies, the cost of the drugs will be more expensive. Therefore, in this research, a prediction of drugs stock in IFRS HKBP Balige using Adaptive Neuro Fuzzy Inference System (ANFIS) will be carried out. ANFIS is a combination of Least Square Estimator (LSE) and Error Back Propagation (EBP) algorithms. ANFIS consists of forward pass and the backward pass learning. The sample data used to predict drugs stock in this research is data of drugs sales at the IFRS HKBP Balige from 2013 to 2015. From the results of drugs stock prediction research with ANFIS, obtained that number of errors of ANFIS model is 5.52%. Based on MAPE accuracy level evaluation, number of errors have an excellent rate so that it can be concluded that the predicted results of the drugs stock are good.
Penerapan Model Pembelajaran dengan Metode Reinforcement Learning Menggunakan Simulator Carla Dharma, Arie Satia; Tambunan, Veronika
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 5, No 4 (2021): Oktober 2021
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v5i4.3169

Abstract

Artificial Intelligence is the study of how to make machines or computer programs have the intelligence or ability to do things that humans can do. The application of AI is currently in various ways, one of which is for self-driving cars. To be able to do a self-driving car, the AI that is implanted in a car must applied to the method to be able to walk on its path and be able to adapt to its environment. Reinforcement learning is one type of machine learning where agents learn something by doing certain actions and the results of those actions and try to maximize the gifts received through interactions with the environment that are reward negative or positive. In this research, we applied of the reinforcement learning method on the Carla Car simulator. The simulator is used to collect data using an RGB sensor, then modeling experiments which produce several models to be used in simulation experiments. The model is obtained by using the Convolutional Neural Network (CNN) algorithm with the NVIDIA architectural model. From the results of research based on experiments conducted obtained the best model obtained from the experimental model by comparing the maximum reward value, high accuracy and low loss is model 1 in the experimental model A with 100 episodes and model 4 in model B experiment with 150 episodes
Survey on Ditenun Application Utilization Through Independent Learning – Independent Campus Program (Merdeka Belajar – Kampus Merdeka) Humasak Tommy Argo Simanjuntak; Arlinta Christy Barus; Samuel Indra Gunawan Situmeang; Arie Satia Dharma
Jurnal Mantik Vol. 5 No. 4 (2022): February: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

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

Abstract

The policy of Independent Learning - Independent Campus (Merdeka Belajar - Kampus Merdeka: MBKM) by the Ministry of Education, Culture, Research, and Technology provides opportunities for students to gain real work experience in an industrial or professional environment to prepare students in social, cultural, work and technological changes. DiTenun (Digital Tenun Nusantara) responds to this challenge by organizing an independent learning program to accelerate student work readiness while increasing the competitiveness of DiTenun’s industry and products. This study aims to evaluate the successful implementation of MBKM in the development of the DiTenun application. The implementation was analyzed from the perspective of students and application users. This study used a survey research method and a saturated sampling technique. Hypothesis testing showed that the implementation of MBKM program positively affects the development of DiTenun application.
PERBANDINGAN ALGORITMA NAIVE BAYES, ID3, DAN TAN PADA KLASIFIKASI SMS SPAM Arie Satia Dharma; Oktavi Yanty Silitonga; H. Justin Manurung
Journal of Maritime and Education (JME) Vol. 1 No. 2 (2019): Article Research, Agustus 2019
Publisher : Politeknik Adiguna Maritim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (387.016 KB)

Abstract

Short Message Service atau SMS merupakan layanan yang memungkinkan untuk mengirim dan menerima pesan dalam bentuk teks pada perangkat telepon genggam. SMS spam adalah pesan yang tidak diinginkan, yang kita tidak ingin pesan tersebut berada di dalam kotak pesan kita. Salah satu cara untuk mengatasi masalah SMS spam tersebut adalah dengan melakukan klasifikasi terhadap teks SMS untuk menentukan spam menggunakan teknik machine learning. Penelitian ini melakukan eksperimen untuk membandingkan tingkat akurasi penerapan metode supervised learning menggunakan algoritma Naive Bayes (NB) Classifier, Iterative Dychotomizer Version 3 (ID3) dan Tree Augmented Naïve (TAN) Bayes Classifier dalam mengklasifikasikan data SMS. Supervised learning merupakan metode untuk mengklasifikasikan data berdasarkan label yang sudah ada. NB adalah klasifikasi data berdasarkan nilai peluang. ID3 adalah algoritma yang melakukan prosedur pencarian secara menyeluruh kepada semua kemungkinan yang akan terjadi. TAN adalah pengembangan dari NB dimana antar node atribut dapat saling memiliki ketergantungan. Berdasarkan hasil eksperimen yang dilakukan, dengan menggunakan pembagian data 70% data training dan 30% data testing diperoleh bahwa algoritma NB menghasilkan akurasi yang paling tinggi sebesar 96.21% dibandingkan dengan kedua algoritma yang lain. Sementara dengan menggunakan pembagian data 80% data training dan 20% data testing diperoleh bahwa algoritma ID3 menghasilkan akurasi yang paling tinggi sebesar 96.47% dibandingkan dengan kedua algoritma yang lain
Socialization of Village Website Management in Pardinggaran Village: Sosialisasi Pengelolaan Website Desa di Desa Pardinggaran Iustisia Simbolon; Arie Satia Dharma; Herimanto Herimanto; Tahan HJ Sihombing; Nenni Mona Aruan; Archico Darius Simpar Sembiring; Enrico Hezkiel Sirait; Wilona Diva Artha Simbolon; Emely Angelica Lestari
Dinamisia : Jurnal Pengabdian Kepada Masyarakat Vol. 8 No. 2 (2024): Dinamisia: Jurnal Pengabdian Kepada Masyarakat
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/dinamisia.v8i2.16509

Abstract

Socialization of village website management in Pardinggaran Village is an activity that helps the government increase awareness among village officials that village websites are a medium for promoting village tourism potential and superior village products. This activity is based on the results of observations on the village website owned by Pardinggaran Village which is still not optimal in managing information related to the village. There is still very little information regarding Pardinggaran Village from the website. Lack of exposure to village activities can be one of the reasons for the lack of tourism development in the village. This is because Pardinggaran Village is a village located in Toba Regency which is a tourist destination area. So, based on these problems, this activity aims to provide innovative solutions in the form of socialization in managing village websites. Through this activity, it is hoped that it can help Pardinggaran Village in managing the village website optimally so that the village website can be used as a medium for information and promotion.
Predictions using Support Vector Machine with Particle Swarm Optimization in Candidates Recipient of Program Keluarga Harapan Arie Satia Dharma; Evi Rosalina Silaban; Hana Maria Siahaan
IAIC International Conference Series Vol. 4 No. 1 (2023): SEMNASTIK 2023
Publisher : IAIC Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/conferenceseries.v4i1.639

Abstract

Program Keluarga Harapan (PKH) is a conditional social assistance program as an effort to alleviate poverty which is allocated to poor vulnerable households. The determination of candidates for the Program Keluarga Harapan assistance recipients is still carried out in village meetings, so it takes quite a long time and there is potential for subjectivity in the assessment carried out by Village Government officials which can lead to differences of opinion between deliberation participants in assessing the eligibility of residents as PKH recipients. For this reason, this research will use an optimization method, namely Particle Swarm Optimization (PSO) to select the most optimal attribute out of 39 attributes. After that, a classification algorithm, namely the Support Vector Machine (SVM), was chosen to form a classification model for Candidates for Social Assistance for the Program Keluarga Harapan (PKH). The classification of Candidates for Social Assistance Recipients of the Program Keluarga Harapan (PKH) was carried out in 2 experiments, namely before and after optimization. Experiments before optimization give an accuracy value of 92.44%. While the Support Vector Machine accuracy value after optimization gives an accuracy value of 92.51%. Based on the experimental results, it can be concluded that the Particle Swarm Optimization method can increase the accuracy of the Support Vector Machine algorithm by 0.07%. And the best model is the Support Vector Machine after optimizing Particle Swarm Optimization by using the 17 most optimized attributes in determining class targets.
Comparison of Feature Extraction Methods on Sentiment Analysis in Hotel Reviews Dharma, Arie Satia; Saragih , Yosua Giat Raja
Sinkron : jurnal dan penelitian teknik informatika Vol. 6 No. 4 (2022): Article Research: Volume 6 Number 4, October 2022
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v7i4.11706

Abstract

The development of technology causes things that done through meet in person or coming to a place can now be done by viewing information through gadgets or websites. Nowadays, to find out information about a place that provides accommodation for a vacation or a business visit, it can be done by accessing social media to see reviews from visitors who have visited the place, example, a hotel. Reviews given by hotel visitors are seen as more credible than information obtained from advertisements but the problem is that there are many reviews circulating on social media and it takes a time to analyze them. This study aims to analyze hotel reviews using the sentiment analysis method with the Support Vector Machine (SVM) approach. Sentiment analysis can be used to analyze the opinions of a large number of hotel visitors where it usually focuses on opinions that positive, negative and neutral. Before being analyzed with the support vector machine algorithm, 3 feature extraction methods will be used, namely Bag Of Words, TF-IDF and improvement TF-IDF to get the value of each word weight. The selection of these three methods is carried out by considering the influence of the presence of the same word feature in each review. In this comparison method, TF-IDF was found to be the best feature extraction method with 71.75% accuracy, 78.66% precision, 71.91% recall and 70.08% f1-score. The results obtained indicate that there are influence of features of the word in the hotel review data.
Comparison of Residual Network-50 and Convolutional Neural Network Conventional Architecture For Fruit Image Classification Dharma, Arie Satia; Sitorus, Judah Michael Parluhutan; Hatigoran, Andreas
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 3 (2023): Article Research Volume 7 Issue 3, July 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i3.12721

Abstract

Classification of fruit images using machine learning technology has had a significant impact on human life by enabling accurate recognition of various fruits. With the advancements in technology, machine learning architectures have become increasingly diverse and sophisticated, providing enhanced capabilities for fruit image classification. However, previous studies have primarily focused on classifying fruits at a basic level. Therefore, there is a growing need for the development and application of Fruit Image Classification systems within the community, particularly in the field of agriculture. Such applications can play a pivotal role in leveraging technology to benefit the agricultural sector, empowering users to gain satisfaction and knowledge regarding different fruits through the utilization of these applications. In this study, we employ both a conventional Convolutional Neural Network (CNN) architecture and a Residual Network-50 for fruit image classification. To ensure robust performance evaluation, the dataset is divided into training and testing subsets, with fruits categorized into specific classes. Furthermore, identical preprocessing and optimization techniques are applied to both architectures to maintain consistency and fairness during the evaluation process. The results of our classification experiments on a dataset consisting of 17 different fruit classes reveal that the conventional CNN architecture achieves an impressive accuracy of 0.998 (99%) with a minimal loss of 0.009. On the other hand, the Residual Network-50 demonstrates a slightly lower accuracy of 0.994 (99%) but with a slightly higher loss of 0.02. Despite the higher loss, the Residual Network-50's accuracy remains comparable to that of the conventional architecture, showcasing its potential for fruit image classification. By leveraging the power of machine learning and these advanced architectures, fruit image classification systems can provide valuable insights and assistance to users. They can facilitate informed decision-making in various domains, including agriculture, food production, and consumer education.
COMPARISON FEATURE EXTRACTION USING ARTIFICIAL NEURAL NETWORK ALGORITHM ON SMOKER PREDICTION Dharma, Arie Satia; Pardede, Cynthia Veronika; Sitorus, Jonggi Vegas
JURTEKSI (Jurnal Teknologi dan Sistem Informasi) Vol 10, No 4 (2024): September 2024
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurteksi.v10i4.2933

Abstract

Abstract: The habit of smoking is dangerous because of the addictive substances that make cigarettes addictive. Its addictive nature poses a significant risk, affecting personality with stress, depression and nervous disorders. Body factors that indicate smoking include blood sugar levels, dental caries, and hemoglobin. To address this, research has been conducted with focused efforts to understand and address the risks associated with smoking and its impact on overall health. This research aims to choose the best method for predicting smokers by using feature selection techniques. The feature selection algorithms uses for that are Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and Genetic Algorithm (GA) to select optimal attributes and uses the k-fold cross validation technique as the validation of the Artificial Neural Network algorithm. The data includes various parameters such as age, height, weight, vision, blood pressure, cholesterol, triglycerides, hemoglobin, AST, ALT, GTP, gender, dental caries and tartar. Hearing ability, urine protein content, and tartar were selected. The results showed that using the Analysis of Variance method showed higher accuracy (77.101%) compared to the Genetic Algorithm method (74.64%) and the Recursive Feature Elimination method (76.08%). Selection of relevant attributes increases the predictions and insights of the Artificial Neural Network model about the effects of smoking on health.            Keywords: artificial neural network; analysis of variance; genetic algorithm; recursive feature elimination; smoker prediction  Abstrak: Kebiasaan merokok berbahaya karena adanya zat adiktif yang membuat rokok menjadi ketagihan. Sifatnya yang membuat ketagihan menimbulkan risiko yang signifikan, mempengaruhi kepribadian dengan stres, depresi, dan gangguan saraf. Faktor tubuh yang mengindikasikan kebiasaan merokok antara lain kadar gula darah, karies gigi, dan hemoglobin. Untuk mengatasi hal ini, penelitian telah dilakukan dengan upaya terfokus untuk memahami dan mengatasi risiko yang terkait dengan merokok dan dampaknya terhadap kesehatan secara keseluruhan. Penelitian ini bertujuan untuk memilih metode terbaik dalam memprediksi perokok dengan menggunakan teknik seleksi fitur. Metode seleksi fitur yang digunakan adalah Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), dan Genetic Algorithm (GA) untuk memilih atribut yang optimal dan menggunakan teknik k-fold cross validation sebagai validasi algoritma Artificial Neural Network. Data tersebut mencakup berbagai parameter seperti umur, tinggi badan, berat badan, penglihatan, tekanan darah, kolesterol, trigliserida, hemoglobin, AST, ALT, GTP, jenis kelamin, karies gigi dan karang gigi. Kemampuan pendengaran, kandungan protein urin, dan karang gigi dipilih. Hasil penelitian menunjukkan bahwa penggunaan metode Analysis of Variance menunjukkan akurasi yang lebih tinggi (77,101%) dibandingkan dengan metode Genetic Algorithm (74,64%) dan metode Recursive Feature Elimination (76,08%). Pemilihan atribut yang relevan meningkatkan prediksi dan wawasan model Jaringan Syaraf Tiruan tentang dampak merokok terhadap kesehatan. Kata kunci: artificial neural network; analysis of variance; genetic algorithm; prediksi perokok; recursive feature elimination
Co-Authors Archico Darius Simpar Sembiring Arlinta Christy Barus Aruan, Nenni Mona Emely Angelica Lestari Enrico Hezkiel Sirait Evi Rosalina Silaban H. Justin Manurung Hana Maria Siahaan Hatigoran, Andreas Herimanto Herimanto Humasak T. A. Simanjuntak Iustisia Simbolon Oktavi Yanty Silitonga Pardede, Cynthia Veronika Purba, Daniel Somanta Samuel Indra Gunawan Situmeang Saragih , Yosua Giat Raja Sitorus, Jonggi Vegas Sitorus, Judah Michael Parluhutan Tahan HJ Sihombing Tambunan, Veronika Tampubolon, Lily Andayani Wilona Diva Artha Simbolon