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All Journal Scientific Journal of Informatics Jurnal Bisnis, Manajemen, dan Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal Elektro dan Telkomunikasi INFOKUM Jurnal Scientia Budapest International Research and Critics Institute-Journal (BIRCI-Journal): Humanities and Social Sciences JPM: JURNAL PENGABDIAN MASYARAKAT International Journal of Engineering, Science and Information Technology Journal of Informatics Management and Information Technology INCODING: Journal of Informatics and Computer Science Engineering Bulletin of Information Technology (BIT) EXPLORER Prosiding Snastikom Journal of Computer Science and Informatics Engineering Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA) Journal of Computing and Informatics Research Journal Of Artificial Intelligence And Software Engineering Jurnal Ilmiah Teknik Informatika & Elektro (JITEK)
Andre Hasudungan Lubis
Universitas Medan Area
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Sistem Informasi Keluar Masuk Barang Berbasis Website Pada Telkom STO Cinta Damai Alex Simanungkalit; Andre Hasudungan Lubis
Jurnal Ilmiah Teknik Informatika & Elektro (JITEK) Vol 2, No 1 (2023): Jurnal Ilmiah Teknik Informatika & Elektro (JITEK)
Publisher : Jurnal Ilmiah Teknik Informatika & Elektro (JITEK)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jitek.v2i1.1895

Abstract

Selama ini proses masuk dan keluarnya barang pada Telkom STO Cinta Damai masih berlangsung secara manual penggunaan aplikasi Microsoft Excel dan pencatatannya masih dilakukan dengan tenaga kerja manusia atau pegawai. Sehingga, proses masuk dan keluarnya barang menjadi kurang efisien dan efektif. Laporan kerja praktek ini membangun sebuah sistem informasi untuk membantu pendataan barang secara daring melalui situs web.  Sistem yang dibangun dengan menggunakan Bahasa pemrograman PHP, dan memanfaatkan aplikasi XAMPP, Sublime Text 3, serta Codeigniter sebagai sumber template. wawancara dan pengawasan dilakukan kepada pihak yang bersangkutan demi memperoleh data relevan untuk dilakukan studi Pustaka dengan proses kerja praktek. Sistem informasi keluar masuk barang bisa digunakan sebagai sarana dan prasarana bagi pegawai Gudang guna mempermudah dalam pengolahan data Gudang serta mempersingkat kerja
Penerapan Multi-Layer Perceptron untuk Mengklasifikasi Penduduk Kurang Mampu Gulo, Senang Hati; Lubis, Andre Hasudungan
Explorer Vol 4 No 2 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v4i2.1146

Abstract

The classification of the less capable population in Afulu Sub-district is currently reliant on a manual system, resulting in prolonged processing times. To address this issue, this research endeavors to develop a practical application for the classification of population data, with the primary objective of expediting the processing of population data in Afulu Sub-district. The study will focus on nine villages within the sub-district, encompassing a total population of 11,722 individuals, with a sample size of 386. The present study utilizes the Multilayer Perceptron, a classical algorithm that continues to be the most widely employed method in numerous researches. The findings of the present study indicate that out of the total sample size, 152 individuals were classified as capable, 86 individuals were classified as moderately capable, and a substantial number of 148 individuals were classified as less capable. The classification results were evaluated using a confusion matrix. The 3-5-1 architecture, comprising of 3 input layers, 5 hidden layers, and 1 output layer, was found to be the most superior. This architecture demonstrated an accuracy value of 96.9%, a recall value of 92%, a precision value of 98.5%, and an F-score value of 94.9%. A detailed elucidation of the parameters employed, the formulas utilized, and several computations performed are explained further.
Implementation of k-means clustering for the job provision in urban village Lubis, Andre Hasudungan; Utami, Widya Rizki; Lubis, Juanda Hakim
Jurnal Matematika Dan Ilmu Pengetahuan Alam LLDikti Wilayah 1 (JUMPA) Vol. 3 No. 1 (2023): March: Mathematics and natural science
Publisher : Lembaga Layanan Pendidikan Tinggi Wilayah I Sumatra Utara (LLDikti I)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54076/jumpa.v3i1.312

Abstract

Unemployment is one of critical issue in society. It may creates snowball effect towards economic development in a country and leads to the economic recessions. Hence, it is important to solve this issue by implementing the clustering to provide groups of people that have chance for job provision. K-Means Clustering is employed in this study by using 378 of data samples. Ages, marital status, amount of land owned, and income are selected as the attributes. The clustering result pointed out that there are 3 clusters that represent the people chances to get job, namely “High”, “Medium”, and “Low”. To evaluate the proposed cluster, Davis-Boulden index is utilized and presents a proper score. The practical implications are presented and discussed, then suggestions for future research are provided.
An Application Of Double Exponential Method For Forecasting Drug Sales Stock Zulhikmah Marpaung; Andre Hasudungan Lubis
Jurnal Scientia Vol. 13 No. 04 (2024): Education and Sosial science, September-December 2024
Publisher : Sean Institute

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

Abstract

The Drug Stock Forecasting Application using a web-based Double Exponential Smoothing method is designed to optimize drug inventory management, particularly at Romora Drugstore. Modern computing technology plays a crucial role in supporting the operational activities of various business sectors, providing quick, precise, and accurate information. This efficiency is especially important in drugstores, where computers assist employees in managing tasks, such as drug inventory. Romora Drugstore, like many others, faces fluctuating monthly drug demands, making accurate forecasting essential to avoid stockouts or overstock situations. To address this challenge, this research proposes the Double Exponential Smoothing method as a forecasting tool. This method predicts future stock requirements based on historical data, enabling better management of drug supplies. By analysed past sales transactions, the application can forecast future demand, helping the drugstore ensure optimal stock levels, prevent financial losses, and enhance overall operational efficiency.
Perancangan Sistem Informasi Pengambilan Nomor Antrian Berbasis Quick Response (Qr) Code pada UPT Samsat Medan Utara Sianipar, Carmenita; Lubis, Andre Hasudungan
Jurnal Ilmiah Teknik Informatika & Elektro (JITEK) Vol 3, No 2 (2024): Jurnal Ilmiah Teknik Informatika & Elektro (JITEK)
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jitek.v3i2.2262

Abstract

This article or writing aims to retrieve queue numbers based on the Quick Response (QR) code. At first the retrieval system was done manually and using paper, this made paper use wasteful. In order to develop and facilitate the queue number retrieval system, this article was created based on a problem focused on quick response (QR) code based queue number retrieval. In order to approach this problem, theoretical references from related journals were used and they went directly to the UPT field. North Medan Samsat. The North Medan One-Stop Manungal Administration System Task Force Implementation Unit (UPT SAMSAT) is one of the SAMSATs in Medan that handles the arrangement of vehicle registration certificates (STNK) both for extending the period and for reversing names. Every day, many visitors come to take care of their STNK and result in visitors having to queue by taking a queue number using paper. Thus causing a lot of paper to be wasted because each queue number that has been taken by the next visitor will be discarded after the visitor's queue is over. The data were collected using actual data and analyzed qualitatively. This study concludes that taking queue numbers is based on a Quick Response (QR) code to make it easier for the public to speed up queues.
Tourist Classification Based on Consumer Behavior Using XGBoost Algorithm zalukhu, Jenius; Hasudungan Lubis, Andre
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14402

Abstract

This study discusses the application of the XGBosst Algorithm to Tourists based on consumer behavior. The purpose of this study is to predict or analyze tourist review data, and to help provide and understand needs so as to improve the quality of services offered. Indonesia has great tourism potential thanks to its natural beauty and cultural diversity. This sector plays an important role in the national economy by creating jobs and encouraging the creative industry and hospitality. The presence of tourists increases regional income through taxes and spending in sectors such as hotels, restaurants, and souvenir shops, as well as creating new jobs. In addition to tourists being able to increase income, there is a need for an understanding of each tourist behavior that is important for the development of adaptive and sustainable tourism.
Grouping of Tourism Locations in Indonesia Using Distance Variations in the K-Means Algorithm Farida, Juni Irsan; Lubis, Andre Hasudungan
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14528

Abstract

Indonesia is home to a diverse range of tourist destinations, yet the classification and mapping of these locations remain a challenge in tourism management. This study aims to cluster tourist destinations in Indonesia by applying the K-Means algorithm with three distance metric variations: Euclidean Distance, Manhattan Distance, and Canberra Distance. The dataset was sourced from public data repositories and underwent preprocessing steps, including data normalization. The optimal number of clusters was determined using the Elbow Method, while the clustering results were evaluated using the Silhouette Score and Davies-Bouldin Index. The findings indicate that Manhattan Distance produced the highest Silhouette Score (0.321463), suggesting superior clustering performance compared to the other two metrics. The results of this study provide valuable insights for stakeholders in formulating strategic tourism promotion and infrastructure development efforts.
Comparison of Support Vector Machine (SVM) and Naïve Bayes Algorithm Performance in Analyzing Garuda Bird Design Sentiment in IKN Moh Hafiz Raja Pratama , Munthe; Andre Hasudungan , lubis
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 8 No. 3Spc (2025): Special Issues 2025: Innovations in Predictive Analytics and Sentiment Analy
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v8i3Spc.14830

Abstract

The Government's policy in moving the Indonesian Capital City (IKN) is considered controversial, this has given rise to various responses from the public, especially on social media X. This research aims to analyze tweet sentiment related to IKN and compare the two algorithms. In this experiment, we succeeded in collecting 5128 tweet data regarding IKN in the X application, the total amount of IKN data was classified into positive sentiment as 2598 1659 negative data and sentiments. Research objectives, methods used, main results, and implications. This research aims to measure public sentiment towards the design of the Garuda bird as the main symbol of the Indonesian Capital City (IKN) by using a comparison of the performance of the Support Vector Machine (SVM) and Naïve Bayes algorithms in analyzing the sentiment of the Garuda bird design in the IKN. main results, for example: the proportion of positive, negative and neutral sentiment, as well as the factors that most influence sentiment. Implications of research results for government, designers and society.
CatBoost Algorithm Implementation for Classifying Women's Fashion Products Madani, Fadillah; Lubis, Andre Hasudungan
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 9 No. 1 (2025): Issues July 2025
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v9i1.15604

Abstract

The rapid growth of the women's fashion industry in the digital era has intensified the need for data-driven approaches to understand customer preferences. This study aims to classify women’s clothing products based on customer reviews by applying CatBoost, a gradient boosting algorithm known for its strong performance with categorical features. The dataset, consisting of 23,486 entries and 11 attributes, was obtained from Kaggle and processed through data cleaning, normalization, exploratory analysis, and model training. Hyperparameter optimization was conducted using Grid Search. Model performance was evaluated using accuracy, precision, recall, and F1-score, and benchmarked against four traditional classifiers: Decision Tree (C4.5), Naïve Bayes, Support Vector Machine (SVM), and K-Nearest Neighbor (KNN). The results show that CatBoost achieved an accuracy of 93.70%, an F1-score of 0.9606, and an AUC of 0.9691, indicating excellent and balanced classification performance. This study demonstrates the effectiveness of CatBoost in handling customer review data and contributes to the development of intelligent product classification systems in the fashion industry
Comparison of KNN and SVM Performance in 2024 Election Results Sentiment Analysis Bukit, M Iqbal Fahilla; Lubis, Andre Hasudungan
Journal of Artificial Intelligence and Software Engineering Vol 5, No 3 (2025): September
Publisher : Politeknik Negeri Lhokseumawe

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30811/jaise.v5i3.7659

Abstract

This study compares the performance of the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) algorithms in sentiment analysis related to the 2024 election results using data from social media. The dataset used consists of 506 public opinion entries categorized into three sentiment labels: positive, negative, and neutral. The data processing involved preprocessing steps such as case folding, tokenization, stopword removal, and stemming, then represented using the Term Frequency–Inverse Document Frequency (TF-IDF) method. The test results showed that both algorithms were able to classify with an accuracy of over 70%. The KNN algorithm produced an accuracy of 75.49%, precision of 71.36%, recall of 75.49%, and an F1-score of 72.88%, while the SVM algorithm showed slightly better performance with an accuracy of 77.45%, precision of 70.59%, recall of 77.45%, and F1-score of 72.15%. Based on the confusion matrix analysis, both models have a high ability to classify positive sentiments, but still face obstacles in recognizing negative and neutral sentiments due to the imbalance in data distribution. Overall, this study indicates that SVM is more suitable for election sentiment analysis on high-dimensional text data.
Co-Authors Alex Simanungkalit Asmah Indrawati Berutu, Elimiana Bukit, M Iqbal Fahilla Dian Noviandri Dinda Rizky Aprillya Elveny, Maricha Erlina Siregar Farida, Juni Irsan Fauziah Rahman Fira Irwannia Gulo, Senang Hati Halawa, Anestin Hartono Hartono Hesti Sabrina Idrus, Syed Zulkarnain Syed Indah Sari Gaurifa Juanda Hakim Lubis Juanda Hakim Lubis Krisdayanti, Sabina Madani, Fadillah Manurung, Dinikxon Moh Hafiz Raja Pratama , Munthe Muhammad Fikri Riandra Muhathir, Muhathir Nurul Khairina Pakpahan, Josua Prayuda Pariyandani, Ayu Rahmad Syah, Rahmad Ronal Maruli Marusaha Sayuti Rahman Sianipar, Carmenita Sihombing, Stevi Freshia Sucitra Dewi SUTRISNO Sutrisno, Nos Utami, Widya Rizki Wan Rizca Amela Wanti, Eka Pirdia zalukhu, Jenius Zealtiel, Billiam Zulfikar Sembiring Zulhikmah Marpaung