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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Journal of Economics, Business, & Accountancy Ventura Journal of Information Systems Engineering and Business Intelligence Tech-E Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Komputasi JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Tekno Kompak Building of Informatics, Technology and Science Kumawula: Jurnal Pengabdian Kepada Masyarakat Jurnal Sistem Informasi dan Informatika (SIMIKA) Jurnal Sisfotek Global Journal of Computer System and Informatics (JoSYC) Community Development Journal: Jurnal Pengabdian Masyarakat IJPD (International Journal Of Public Devotion) Jurnal Teknologi dan Sistem Tertanam Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Data Mining dan Sistem Informasi Jurnal Teknologi dan Sistem Informasi Journal Social Science And Technology For Community Service J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Sisfotek Global COMMENT: Journal of Community Empowerment Journal of Engineering and Information Technology for Community Service Jurnal Ilmiah Edutic : Pendidikan dan Informatika Jurnal Pengabdian kepada Masyarakat (Nadimas) Jurnal Media Borneo Jurnal Informatika: Jurnal Pengembangan IT Jurnal Media Celebes Journal of Artificial Intelligence and Technology Information Journal of Information Technology, Software Engineering and Computer Science The Indonesian Journal of Computer Science
Alita, Debby
Universitas Teknokrat Indonesia
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Other

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Software Development Sistem Informasi Kursus Mengemudi (Kasus: Kursus Mengemudi Widi Mandiri) Nugroho, Nurhasan; Rahmanto, Yuri; Rusliyawati, R; Alita, Debby; Handika, H
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 1 (2021): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i1.325

Abstract

Widi Mandiri is an institution that provides training courses for driving four-wheeled vehicles. In managing data, Widi Mandiri uses records in certain forms or books from participant registration, scheduling and all business processes. With the existing system, there are several obstacles that hinder the conduct of business at Widi Mandiri. For that we need a driving course information system that helps in the management of all business activities at Widi Mandiri. The system development is carried out by using the extreme programming (XP) system development approach. The research has produced a system that can manage driving course data from registration of course participants, booking courses and cars to scheduling according to the schedule of course participants and instructors. Based on usability testing, the average value was 85.8%, and was in the good category
Software Development Sistem Informasi Kursus Mengemudi (Kasus: Kursus Mengemudi Widi Mandiri) Nugroho, Nurhasan; Rahmanto, Yuri; Rusliyawati, R; Alita, Debby; Handika, H
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 5, No 1 (2021): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v5i1.325

Abstract

Widi Mandiri is an institution that provides training courses for driving four-wheeled vehicles. In managing data, Widi Mandiri uses records in certain forms or books from participant registration, scheduling and all business processes. With the existing system, there are several obstacles that hinder the conduct of business at Widi Mandiri. For that we need a driving course information system that helps in the management of all business activities at Widi Mandiri. The system development is carried out by using the extreme programming (XP) system development approach. The research has produced a system that can manage driving course data from registration of course participants, booking courses and cars to scheduling according to the schedule of course participants and instructors. Based on usability testing, the average value was 85.8%, and was in the good category
Perbandingan Algoritma SVM, Random Forest, KNN untuk Analisis Sentimen Terhadap Overclaim Skincare pada Media Sosial X Rahmawati, Ira Tri; Alita, Debby
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6782

Abstract

The cosmetic industry in Indonesia, especially skincare products, is growing rapidly along with changes in people's lifestyles and technological advances. One of the main issues that arise is overclaiming, which can harm consumers and damage the company's reputation. This study aims to compare the performance of three algorithms in sentiment analysis of skincare overclaims on X social media. The evaluated algorithms include Support Vector Machine (SVM), Random Forest, and K-Nearest Neighbors (KNN). The research dataset consists of 7,774 tweets collected between October 1 and November 30, 2024, with 5,559 tweets after the preprocessing stage, consisting of 4,281 negative sentiment tweets and 1,275 positive sentiment tweets. Data imbalance was addressed using the Synthetic Minority Over-sampling Technique (SMOTE), with 80% data split for training and 20% for testing. The results showed that before the application of SMOTE, the Random Forest algorithm had the highest accuracy of 95%, followed by Support Vector Machine at 91% and K-Nearest Neighbors at 80%. After the application of SMOTE, the accuracy increased significantly, with Random Forest reaching 98%, Support Vector Machine 97%, and K-Nearest Neighbors 84%. Random Forest proved to be the best algorithm, with the highest performance before and after SMOTE implementation, and was effective in handling both sentiment classes. This research provides insights for the skincare industry and regulators to detect and address product over-claiming issues through machine learning-based approaches.
Perbandingan Algoritma Naïve Bayes dan Random Forest untuk Melakukan Analisis Sentimen Cyberbullying Generasi Z Pada Twitter Danuarta, Ervin; Alita, Debby
Building of Informatics, Technology and Science (BITS) Vol 6 No 4 (2025): March 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i4.6909

Abstract

Cyberbullying is a significant social problem, especially for Generation Z,who actively use social media such as Twitter, Instagram and TikTok. It has a very negative impact on the victim's mental health, such as a sense of isolation, loss of confidence, and insecurity. This study aims to compare the performance of two machine learning algorithms, namely Naive Bayes and Random Forest, in sentiment analysis related to cyberbullying in Generation Z through the Twitter platform. The research method involved collecting and preprocessing data from 5505 tweets, which were then divided into training data (80%) and test data (20%). The research also applied Synthetic Minority Oversampling Technique (SMOTE) to overcome data imbalance. Preliminary results show that before the application of SMOTE, Naïve Bayes had an accuracy of 92% and Random Forest reached 94%. After the application of SMOTE, the performance of both algorithms changed. Naive Bayes accuracy decreased to 89%, with precision increasing from 92% to 99% for negative sentiments, but recall dropped from 100% to 79%, resulting in an F1-Score of 88%. In contrast, Random Forest showed significant improvement, with accuracy reaching 100%, precision and recall for negative sentiment remaining 100%, and F1-Score increasing from 97% to 100%. This study concludes that Random Forest, with the application of SMOTE, provides more stable and effective performance than Naive Bayes in cyberbullying sentiment analysis. These results are expected to support the development of text analysis technology and efforts to prevent cyberbullying in Generation Z.
Hybrid G2M Weighting and WASPAS Method for Business Partner Selection: A Decision Support Approach Wang, Junhai; Setiawansyah, Setiawansyah; Alita, Debby
Journal of Computer System and Informatics (JoSYC) Vol 6 No 3 (2025): May 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i3.7229

Abstract

Choosing the right business partner is a crucial factor in the success and continuity of a company's operations. The main issue in selecting business partners is the complexity of balancing various interconnected and often conflicting factors. Another problem lies in the subjectivity and limitations of information. Evaluators or decision-makers may have differing views on the priority of criteria or the interpretation of the available data. This study proposes a hybrid method-based decision support system approach that combines G2M Weighting and WASPAS to address the challenges in complex and uncertain multi-criteria evaluations. The G2M method is used to objectively determine the weight of criteria based on geometric averages in gray environments, so as to be able to capture data variability and uncertainty. Furthermore, the WASPAS method is applied to calculate the final value and rank the alternative business partners based on a combination of additive and multiplicative approaches. The ranking chart for business partner selection using the G2M Weighting and WASPAS method shows that Partner G gets the highest score of 9.93E+03, followed by Partner A and Partner E who have the same score of 9.43E+03. Meanwhile, Partner D had the lowest score, which was 5.97E+03. This ranking of business partner selection shows that Partner G is the best choice as a business partner based on the evaluation method used. The results of the study show that this hybrid approach provides more accurate, stable, and comprehensive evaluation results than conventional methods. This approach can be an effective solution for companies in supporting the strategic decision-making process in choosing the best business partners.
Penerapan Oversampling Pada Klasifikasi Ujaran Kebencian Menggunakan Bidirectional Encoder Representations from Transformers Syahwaluddin, Risal; Alita, Debby
The Indonesian Journal of Computer Science Vol. 13 No. 4 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i4.4295

Abstract

The problem of class imbalance is a common challenge in classification model building, especially in the context of hate speech. This study evaluates the effectiveness of the SMOTE oversampling technique in improving the performance of hate speech classification models using BERT. The dataset used has significant class imbalance, with the largest number of samples in the Hate class, followed by Offensive, and Neither. Two experiments were conducted: one without using SMOTE and one with SMOTE applied. Results showed that the application of SMOTE improved the overall model accuracy from 85% to 88%. Precision for the Offensive minority class increased from 0.33 to 0.45, although recall decreased from 0.45 to 0.28. In the Neither class, the F1-score increased, indicating an improvement in the balance between precision and recall. Performance on the majority Hate class remained stable, indicating that SMOTE did not interfere with the model's performance on the already dominant class. Overall, the application of SMOTE provides significant benefits in handling class imbalance, especially in improving precision for minority classes, resulting in a more accurate classification model.
Online Marketing Readiness of MSMEs in Indonesia: A Perspective of Technology Organizational Environmental Framework Asmawati, Asmawati; Ahmad, Imam; Suwarni, Emi; Alita, Debby; Hasrina, Cut Delsi
Journal of Economics, Business, and Accountancy Ventura Vol. 27 No. 1 (2024): April - July 2024
Publisher : Universitas Hayam Wuruk Perbanas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14414/jebav.v27i1.3399

Abstract

Micro, Small, and Medium-sized Enterprises (MSMEs) play a crucial role in the economic landscape of tourism areas, offering collective services that enhance tourist experiences and convenience at destinations. One key effort to promote tourism is enhancing the technological readiness of MSMEs. This study aims to evaluate the preparedness of MSMEs to adopt e-commerce technology. Data was gathered through interviews and questionnaires distributed randomly to 184 business operators. The research utilized the Technology Organizational Environmental (TOE) framework, encompassing ten indicators identified from various sources. Data analysis was conducted using Structural Equation Modeling (SEM) with four hypotheses. The findings reveal that both organizational readiness and external environmental support have a positive impact on technology adoption readiness. Furthermore, organizational readiness significantly mediates the relationship between environmental support and technological readiness. Therefore, it is essential to develop the organizational readiness of MSMEs to facilitate the adoption of e-commerce technology.
Analisis Opini Publik Tentang Boikot Produk Pro-Israel di Twitter Berbahasa Indonesia Menggunakan Metode SVM alifa, Chairunnisa fadia; Alita, Debby
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 2 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i2.6559

Abstract

The century-long Israeli-Palestinian conflict has created diverse opinions in Indonesian society. The escalation of tensions in Gaza triggered calls for boycotts of products suspected of supporting Israel. In this study, a Support Vector Machine (SVM) method is used to analyze sentiment on Twitter related to pro-Israel boycotts. By understanding public opinion, this study evaluates the performance of SVM with linear kernel and RBF. Data collection was done through crawling Twitter with the keyword "Pro-Israel boycott", resulting in 2600 data. Data preprocessing involved case folding, cleaning, stopwords, stemming, and TF-IDF weighting. Manual labeling was done for 1560 support data and 1040 non-support data. Implementation of the SVM model resulted in 92.5% accuracy for the linear kernel and 91.92% for the RBF kernel. Word cloud analysis provided visualization of key words and sentiments related to the boycott. This research shows the dominance of positive sentiment with 1560 positive tweets and 1040 negative tweets. For development, it is recommended to add sentiment analysis methods, use a wider dataset, and consider supporting variables to improve accuracy and understanding of public sentiment on the issue.
Implementasi Metode SVM Pada Sentimen Analisis Terhadap Pemilihan Presiden (Pilpres) 2024 Di Twitter anggraini, jenny; Alita, Debby
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 2 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i2.6560

Abstract

The focus of the research is the use of Twitter as a platform to express the political opinions of the Indonesian people regarding the 2024 Presidential Election. By utilizing sentiment analysis using the Support Vector Machine (SVM) method, this research aims to evaluate the accuracy of SVM in classifying tweets and compare the performance of four types of SVM kernels. Visualizations of positive and negative sentiments are also generated to provide a clearer picture. The stages of the research involve Twitter data collection, and pre-processing with steps such as data cleansing, case folding, tokenizing, stemming, and filtering. Labeling is done to identify sentiment, then feature extraction using TF-IDF. SVM implementation with linear, polynomial, RBF, and sigmoid kernels is performed, followed by model evaluation using precision, recall, F-measure, and accuracy metrics. The study used SVM to analyze the sentiment of the 2024 presidential election on Twitter data. As a result, out of 3938 tweets, 1575 were positive and 2363 were negative. The SVM model achieved 95.05% accuracy, superior in predicting negative sentiment. Comparison of SVM kernels shows the highest accuracy in the linear kernel 95.43%. Sentiment analysis on tweets shows a majority of positive support for Ganjar 54.9%, while Anies and Prabowo have support levels of 15.8% and 29.3% respectively.
Perbandingan Metode Naive Bayes Classifier dan Support Vector Machine Pada Analisis Sentimen Wisata Biru Berdasarkan Ulasan Twitter, Instagram, dan Google Maps Review Rahmadila, Selvi; Alita, Debby
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8791

Abstract

Blue tourism in Lampung Province has been recognized as a leading regional asset encompassing coastal areas, islands, and marine zones with strong appeal to visitors. Public responses toward these destinations can be captured through online reviews distributed across multiple digital platforms. In this study, the performance of sentiment classification algorithms, namely Naive Bayes Classifier and Support Vector Machine, was examined and compared using reviews related to blue tourism in Lampung. A total of 3,950 review records were collected from Twitter or X, Instagram, and Google Maps Review. The collected data were subjected to a series of preprocessing stages, including text cleaning to remove irrelevant elements, followed by theme and sentiment labeling using a semi supervised learning approach. Feature representation was generated through the Term Frequency Inverse Document Frequency method to transform textual data into numerical form. The labeling results revealed an imbalanced sentiment distribution with a strong dominance of positive sentiment. Model evaluation was conducted using an 80 to 20 split between training and testing datasets. The evaluation results indicated that the Support Vector Machine achieved an accuracy of 91.90 percent, while the Naive Bayes Classifier reached an accuracy of 90.38 percent. These findings suggest that the Support Vector Machine demonstrates superior capability in handling high dimensional textual data and imbalanced sentiment distributions. The outcomes of this study are expected to provide empirical guidance in selecting appropriate sentiment analysis algorithms to support data driven management and development of blue tourism destinations.
Co-Authors ., Rusliyawati Ade Dwi Putra Ade Setiawan alifa, Chairunnisa fadia Andi Kurniawan Andi Nurkholis Anggraini, Jenny Aris Munandar Asmawati Asmawati Asmawati Asmawati Bambang Satrio Gandhi Cholid Fadilah Hasri Danuarta, Ervin Dedi Darwis Dinda Titian Lestari Dyah Aminatun Dyah Ayu Megawaty Dyah Ayu Megawaty Emi Suwarni Erliyan Redy Susanto Fadila Shely Amalia Feby Amanda Fenty ariany Handika, H Hasrina, Cut Delsi Hayatunnufus Hayatunnufus Heni Sulistiani Ika Septiana Imam Ahmad Imam Ahmad Indah Sari Irwan Tubagus Isnain, Auliya Rahman Jepi Supriyanto Krisna, Anan Lathifah L Lathifah Lathifah Lisa Ariyanti M Febrian As Shidiq Mandasari, Berlinda Marga, Nurman Satya Miswanto Miswanto Muhammad Najib Dwi Satria Muhammad Pajar Kharisma Putra Muhammad Saefudin Muhtad Fadly Munti Sarida Nur Rokhman Nurhasan Nugroho Nurman Satya Marga Nuroji Nuroji Permata Permata Prita Dellia Puspaningtyas, Nicky Dwi Pustika, Reza Putra, Ade Dwi Putri Sukma Dewi Rahmadila, Selvi Rahmawati, Ira Tri Rani, Ni Made Suka RB Ali Shodiqin Rillya Arundaa Riska Amalia Praptiwi S. Samsugi Sakti, Adam Indra Salsabilla, Tasya SANTIKA, YULI Saputri, Gita Aprinda Sari, Raras Kartika Sena, Fikih Yuhada Setiawansyah Setiawansyah Sigit Priyanta Styawati Styawati Styawati, Styawati suaidah suaidah Suhadi, Miki Surahman, Ade Suwarni, Emi Syahwaluddin, Risal Taufiq Martiwansyah Tenda, Edwin Wang, Junhai YOHANA TRI UTAMI, YOHANA TRI Yuri Rahmanto Yusra Fernando