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Bustami Bustami
Teknik Informatika Universitas Malikussaleh
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Journal : International Journal of Engineering, Science and Information Technology

 1 
Emarketplace Performance Analysis Using PIECES Method Munirul Ula; Rizal Tjut Adek; Bustami Bustami
International Journal of Engineering, Science and Information Technology Vol 1, No 4 (2021)
Publisher : Master Program of Information Technology, Universitas Malikussaleh, Aceh Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (360.5 KB) | DOI: 10.52088/ijesty.v1i4.138

Abstract

E-Marketplace is a place in cyberspace where prospective buyers meet each other to conduct transactions electronically through the internet medium. Like the market in the conventional sense, namely a meeting place for sellers and buyers, in the E-Marketplace, various companies in the world also interact without being limited by the territory of space (geography) and time. Therefore, an analysis of the performance of the website is needed to ensure the performance of the Bireuen emarketplace (meukat.com) website can run effectively in the future. The role of this emarketplace is very important, therefore in building emarketplace we must pay attention to several factors, namely: performance, information, economic, control, efficiency, and service, which is better known as the PIECES method. To analyze the performance of our self-developed emarketplace, was done by PIECES method. While the testing method in the performance analysis of the website uses the GTMetrix and Google Transparency applications. The results of the PIECES questionnaire on the dimensions of Information, Economy, Efficiency, and Service. The average score for the all dimensions is moderate, it is ranging from 42.8% to 51.45% and is in line with the expectations. The GTMetric test results of the Emarketplace website, shows that the average performance grade is 66% or grade D. This means that the quality of the Emarketplace website based on the index generated by Google is still low. It should be improved to provide good quality of service for users in future. The Emarketplace are also being analyzed by the Google transparency report, the result is “no unsafe content” was found, means this website is safe to visit. There are no applications that harm the users.
Analysis of Public Sentiment Towards Celebrity Endorsment On Social Media Using Support Vector Machine Syahputra, M Oriza; Bustami, Bustami; Rosnita, Lidya
International Journal of Engineering, Science and Information Technology Vol 4, No 3 (2024)
Publisher : Department of Information Technology, Universitas Malikussaleh, Aceh Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i3.543

Abstract

Analysis of public sentiment towards celebrity endorsements on social media is very important to understand the public's response to promotional campaigns involving celebrities. In this study, we combine the VADER labeling method with the Support Vector Machine (SVM) method to analyze public sentiment toward celebrity endorsements on social media. Data is taken from various social media sources such as Twitter, Instagram, and Facebook. The data is pre-processed to ensure data accuracy and relevance and then labeled with the VADER method to determine the positive, negative, or neutral sentiment of the text. The labeled data is then extracted for features and used to train the SVM model. The trained SVM model is then validated using test data to measure its accuracy and performance. The results of the analysis provide useful insight into public sentiment towards celebrity endorsements on social media and can provide recommendations for stakeholders regarding this matter. Overall, combining the VADER labeling method with SVM in analyzing public sentiment towards celebrity endorsements on social media shows more accurate results and can provide practical benefits in marketing and promotional strategies. The results shown using the Support Vector Machine method with a ratio of 80:20 can provide average precision results of 77%, recall of 100%, f1-score of 87%, and accuracy of 76.92%. Twitter application user sentiment shows that 77% (338 data) of Twitter user reviews provide positive sentiment and 23% (119 data) provide negative sentiment reviews from a total of 517 data. Suggestions from researchers are that in future research they can add more data to make modeling easier to provide higher accuracy values. Using other classification and performance evaluation methods, such as Naive Bayes, Decision Tree, Fuzzy, or Deep Learning. Use other data processing tools, such as RapidMiner, Jupyter Notebook, RStudio, or others.
Expert System For Diagnosis of Mental Health Disorders in Students Using Case-Based Reasoning Method With a Web-Based Positive Psychology Approach Bancin, Udurta; Bustami, Bustami; Rosnita, Lidya
International Journal of Engineering, Science and Information Technology Vol 4, No 4 (2024)
Publisher : Department of Information Technology, Universitas Malikussaleh, Aceh Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v4i4.592

Abstract

Mental health issues among students have become a significant concern affecting their quality of life and academic performance. An effective expert system is needed to diagnose and provide appropriate interventions. This research develops a web-based expert system that utilizes the Case-Based Reasoning (CBR) method combined with a positive psychology approach to diagnose mental health disorders in students. The CBR method identifies similarities between new and previous cases, while the positive psychology approach focuses on individual strengths and potential for growth. The system integrates a database of student mental health cases and CBR algorithms to produce relevant diagnoses. This study investigates four types of mental health disorders: panic, anxiety, stress, and depression. The method used for data analysis is Case-Based Reasoning. The diagnosis results are based on calculations from symptom choices within the system, where each symptom has a weight. The highest similarity calculation obtained from past cases is used as a solution to address the problem. System testing, based on expert knowledge with 15 test data samples categorized by mental health disorders and 38 symptoms, achieved an accuracy rate of 85%.
Application of K-Medoids Clustering Method on Disease Clustering Based on Patient Medical Records Fatika, Dian; Bustami, Bustami; Afrillia, Yesy
International Journal of Engineering, Science and Information Technology Vol 5, No 1 (2025)
Publisher : Malikussaleh University, Aceh, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52088/ijesty.v5i1.679

Abstract

Dr. Fauziah Bireuen Regional General Hospital (RSUD) faces daily challenges in managing the ever-increasing medical record data. Currently, the medical record data only consists of reports containing information on the number of patients and their diseases, which are then archived without further processing to generate valuable information. This research aims to cluster diseases based on patient medical records using the K-Medoids Clustering method, thereby providing information on the patterns of disease spread in various regions of the Bireuen Regency. The data used are patient medical records from RSUD Dr. Fauziah Bireuen from 2021–2023, focusing on five common diseases: stroke, hypertension, schizophrenia, dyspepsia, and pneumonia. We conducted Clustering in 17 sub-districts in Bireuen Regency using the K-Medoids method and determined the optimal number of clusters using the Elbow method. The research results show that the K-Medoids method successfully grouped each disease into 3 clusters: high, medium, and low. The results showed that the K-Medoids method successfully grouped each disease into 3 clusters: high, medium, and low. The cluster distribution for stroke disease consists of 7 sub-districts in the high cluster, 7 in the medium, and 3 in the low. Hypertension disease consists of 6 sub-districts in the high cluster, 3 in the medium, and 8 in the low. Schizophrenia disease comprises seven sub-districts in the high cluster, 8 in the medium, and 2 in the low. Dyspepsia disease includes six sub-districts in the high cluster, 2 in the medium, and 9 in the low. Meanwhile, pneumonia disease consists of 8 sub-districts in the high cluster, 5 in the medium, and 4 in the low.
Co-Authors Abd Mubaraq, S.E.,Sy. M.A Achmad Achmad Ahmad Farhan, Ahmad Aidilof , Hafizh Al Kautsar Aidilof, Hafizh Al Kausar Ainal Mardhiah Alfyansyah, Gusti Alhamidiy, Abdillah Nur Ali Masduqi Amiruddin Amiruddin anasril, anasril Ardelia, Aya Sofia Aris Munandar Asmirayani Asmirayani Asmirayani, Asmirayani Auliaurrahman, Auliaurrahman Azhar Azhar Azrai Putra Barumun Daulay Azzahra, Dea Bagas Wahyu Adhi, Bagas Wahyu Baharuddin Baharuddin Bancin, Udurta Custer, Jhonny Cut Farida Aryani Dea Azzahra Desiana Desiana, Desiana Dewiyani, Evi Dwi Kurniawan, Dwi Ekajaya, Candra Endah Sri Wahyuni Endang Susilawati Fadli Fadlisyah Fadlisyah Fahrul Rozi Siregar Fajriana, Fajriana Fasdarsyah Fasdarsyah Fatika, Dian Fiasari, Fiasari Gusti Alfyansyah Gusti Alfyansyah Hafidh Rafif, Teuku Muhammad Hakim, Kurniawati Halimatussakdiah Halimatussakdiah Hanum, Nisrina Harahap, Elly Wanisyah Harahap, Ilham Taruna Harahap, Samsul Bahry Harun, Muhazar Hasan Sazali ilham - sahputra Indrayani Indrayani Irma Mauliza Iskandar, Fahra Azzahra Ismail Ismail Joko Santoso Jufrizal Jufrizal Junita, Dini Karimullah Karimullah Katimin, Katimin Kautsar, Hafizh Al Kembaren, Emmia Tambarta Kholil, Syukur Laksamana, Rio Lenny Hasan, Henny Sjafitri, Nelharosma, Lidya Rosnita Luthfiah, Moulana M Fauzan Maharani, Silfa Mailin, Mailin Maryono Maryono Maulidi Maulidi Mauliza, Irma Miftahul Jannah Muhammad Fajri Muhammad Fauzan Muhammad Fikry Muhammad Hutomi MUHAMMAD KOSIM, MUHAMMAD Muhammad Muhammad Muhammad Muhammad Muhammad Yani, Muhammad Mukhlish Muhammad Nur Mukti Qamal Mulyani, Tasya Munirul Ula Murni, Mayang Mutammimul Ula Muthmainnah Muthmainnah Nastasya, Widia NELI SUSANTI, NELI Ngadimin Ngadimin Nilda, Elvi Nisa, Cut Chairun Nurdin Nurdin Nurhayani, Melda Vitry Nyak Arief, Teuku Okataria, Welan Pramadani, Kendra Putra Barumun Daulay, Azrai Putri, Dea Rizka Amanda Putri, Mia Yolanda Utami Putri, Rahmatillah Dwi Rahmah Hayati Rahmiati Rahmiati Razi, Ar Rian Diana Ridhwan Ridhwan, Ridhwan Ridwan, Ridwan Rini Fitriani Rini Meiyanti Risawandi, Risawandi Rizal Rizal Rizal Rizal S.Si., M.IT, Rizal Rizki Putri Ramadhani, Rizki Putri Rizki, Agus Maula Rosyanne Kushargina Rozzi Kesuma Dinata RR. Ella Evrita Hestiandari Rusdi Rusdi Safri, Hayanuddin Said Fadlan Anshari Sakdah, Nurul Salhan, Susi Samosir, Dini Kairiyah Samosir, Hasrat Efendi Sari, Efti Novita Sayed Fachrurrazi Senda, Tito Afwi Sepris Yonaldi, Sepris Yonaldi Setyarini Soemadi, Dian Shayravi Shayravi Shayravi, Shayravi Siregar, Fahrul Rozi Siti Nurhaliza Siti Sahara Suci Fitriani Suharyon, Suharyon Sujacka Retno Sumanti, Eva Suryadi Suryadi Suryana Suryana Suryana Suryana Syafruddin Nurdin syah, Fadli Syahbudi, Syahbudi Syahputra, M Oriza Syahrial, Dopa Syamsarina, Syamsarina Syibral Malasyi T.M. Rafsanjani Teuku Mudi Hafli Tri Mulyono Tri Mulyono H, Khairunnisak Utama, Lalu Juntra Yacoub, Yarlina Yesi Meldawati Yesy Afrillia Yulia Fitri Yunianto, Andi Eka Yunina Yunina Zainal Arifin Zara Yunizar Zubir, Zubir Zuleha, Zuleha Zulkarnen Mora, Zulkarnen Zulmi, Syafriade