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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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Perbandingan Random Forest dan SVM dalam Analisis Sentimen Quick Count Pemilu 2024 septiana, ika; Alita, Debby
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 3 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i3.6640

Abstract

The implementation of the 2024 elections is regulated in the General Election Commission Regulation (PKPU) Number 3 of 2022, which also stipulates the election schedule and stages.After the simultaneous general elections that took place on February 14, 2024, problems arose among the public regarding the Quick Count results, especially for the Presidential election.The Quick Count results themselves generated various opinions, both positive and negative.In the post-election Twitter page, there are many conversations in cyberspace related to the Quick Count results on Twitter. Thus, sentiment analysis can be used to classify tweets and comments about the 2024 election quick count results into three categories, namely positive, negative, and neutral.Thus, this analysis is expected to provide some significant benefits related to the quick count results in the 2024 election. Random Forest and Support Vector Machine are two machine learning techniques used to measure how accurate the resulting sentiment analysis is. From the results of the research that has been carried out, there are 2000 data collected during February 2024. After preprocessing and labeling, there are 1,116 positive class data, 730 negative class data and 154 neutral class data.From the results of the comparison of the algorithms evaluated, the accuracy value of the two algorithms was obtained.The Random Forest algorithm produces an accuracy of 78%, while the SVM algorithm produces an accuracy of 80%.This shows that in sentiment analysis on the 2024 election quick count, the SVM method obtained a greater accuracy value compared to Random Forest.
Analisis Sentimen Inses di Social Media menggunakan Algoritma Naïve Bayes Salsabilla, Tasya; Alita, Debby
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 3 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i3.6611

Abstract

Sexual violence, especially against women and children, is a serious problem in Indonesia. Cases are increasing every year, including incest, which involves sexual relations between close family members. Girls, who are often considered weak and vulnerable, are the main victims. The latest data from the National Commission on Violence Against Women records a decrease in incest cases from 1,210 in 2017 to 215 in 2020. However, attention is still needed, especially because biological fathers are the largest perpetrators. This research uses the Naïve Bayes algorithm for sentiment analysis. This algorithm is an effective classification method based on Bayes' theorem with simple assumptions but is quite effective. Assuming that each feature in the data is independent, Naïve Bayes can work well in text analysis. The research results showed an accuracy rate of 94%. Continued attention to sexual violence, especially incest, is needed to protect vulnerable girls. Protection efforts must continue to be improved, including the application of sentiment analysis methods such as Naïve Bayes for monitoring and early detection. Public awareness and cross-sector cooperation are also key in overcoming this phenomenon.
Analisis Sentimen Twitter Terhadap Pemindahan Ibu Kota Negara Menggunakan Support Vector Machine Saputri, Gita Aprinda; Alita, Debby
Jurnal Informatika: Jurnal Pengembangan IT Vol 9, No 3 (2024)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v9i3.6612

Abstract

The Indonesian government announced plans to move the capital from Jakarta to East Kalimantan due to the high population burden and economic contribution on the island of Java. Statistical data shows that the island of Java has a large population, reaching 151.59 million people or around 56.10% of the total population of Indonesia, and will provide a large participation in national GDP in 2021. Moving the capital city is seen as a step. . for the sake of equal distribution of population and economy throughout Indonesia. Rapid urbanization on the island of Java, especially in the buffer areas of the capital city of Jakarta, is one of the main reasons behind this decision. This research uses data from the social media platform Twitter to analyze sentiment using 2 categories, namely positive and negative sentiment regarding the relocation of the National Capital, analyzed using the Support Vector Machine method. In this study, the SVM kernel type was used, namely a linear kernel with an accuracy of 92.70%, then improved with Stratified k-Fold Cross Validation, getting 100% accuracy in iterations 1 and 5. The classification results using the Support Vector Machine method are statedthat the linear kernel has better accuracy. This sentiment analysis provides insight into the public's views on the proposed measure. This research can be used as material for consideration of future government policy regarding relocating the capital city.
IoT-based Smart Pond Controller to Optimize Freshwater Fish Production and Data Management Application to Improve the Quality of Administration of Freshwater Fish Farmers in Pagelaran, Pringsewu Regency Lampung Alita, Debby; Surahman, Ade; Sarida, Munti
International Journal of Public Devotion Vol 7, No 2 (2024): August - December 2024
Publisher : STKIP Singkawang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26737/ijpd.v7i2.6034

Abstract

The main problems that are a priority to be resolved are: 1) Systems that are still manual; 2) Often experience large losses due to poor water quality (high water pH); 3) Records of fish feed processing and financial management which are still done manually. Based on the priority problems faced by partners, the solutions offered by the proposing team for this PKM scheme are: 1) Implementing an IoT-based Smart Pond: Water Quality Control Tool, Automatic Feeding, IoT-Based Predator Detection; 2) Implementing Smart Pond: Web-based cultivation data management application; 3) Providing educational training on freshwater fish cultivation for breeders and all EWAK POND employees. The result of this service is that by implementing IoT technology, automatic feeding can reduce the amount of daily feed so that it can save farmers' expenses, reduce mortality rates, and improve fish farming management. Apart from that, this program also aims to empower local breeders through training and mentoring, so that they can better understand and utilize this technology for the sustainability of their businesses
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
ANALISIS SENTIMEN MASYARAKAT TERHADAP KASUS JUDI ONLINE MENGGUNAKAN DATA DARI MEDIA SOSIAL X PENDEKATAN NAIVE BAYES DAN SVM As Shidiq, M Febrian; Alita, Debby
Jurnal Sistem Informasi dan Informatika (Simika) Vol 8 No 1 (2025): Jurnal Sistem Informasi dan Informatika (Simika)
Publisher : Program Studi Sistem Informasi, Universitas Banten Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47080/simika.v8i1.3624

Abstract

Research conducted by analyzing public sentiment related to online gambling cases using datasets from x social media using the naïve bayes method approach and support vector machine (SVM). The analysis phase starts with data gathering or crawling, followed by data labeling, data preprocessing, and ultimately method categorization. The dataset comprises 2,866 tweets, with 1,436 classified as positive (50.12%) and 1,429 as negative (49.88%). The data before to the classification process is partitioned into training data and testing data, including 70% training data and 30% testing data. The analysis with the SVM approach yielded a classification accuracy of 83%, whereas the naïve Bayes method achieved just 79%. Upon completion of the method classification process, the subsequent phase involves visualization and assessment. During the visualization step, bar plots, word clouds, and word frequencies derived from sentiment analysis calculations are shown, alongside a visualization of words from the dataset. The investigation indicates that the SVM approach outperforms Naive Bayes in sentiment classification. The benefit of SVM resides in its capability to manage data with elevated limits and accuracy, enhancing its efficiency in discerning positive and negative thoughts. The findings of this study demonstrate that SVM is better appropriate for data exhibiting complicated distributions, whereas the Naive Bayes approach yields suboptimal results. Thus, SVM can be proposed as a more appropriate and reliable approach for similar sentiment analysis in the future.
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.
Co-Authors ., Rusliyawati Adam Indra Sakti Ade Dwi Putra Ade Setiawan Andi Kurniawan Andi Nurkholis Aris Munandar As Shidiq, M Febrian Asmawati Asmawati Asmawati Asmawati Bambang Satrio Gandhi Chairunnisa fadia alifa 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 jenny anggraini Jepi Supriyanto Krisna, Anan Lathifah L Lathifah Lathifah Lisa Ariyanti Mandasari, Berlinda Miswanto Miswanto Muhammad Najib Dwi Satria Muhammad Pajar Kharisma Putra Muhammad Saefudin Muhtad Fadly Munti Sarida Nur Rokhman Nurhasan Nugroho Nurman Satya Marga Nurman Satya Marga Nuroji Nuroji Permata Permata Prita Dellia Puspaningtyas, Nicky Dwi Pustika, Reza Putra, Ade Dwi Putri Sukma Dewi Rahmawati, Ira Tri Rani, Ni Made Suka RB Ali Shodiqin Rillya Arundaa Riska Amalia Praptiwi S. Samsugi Salsabilla, Tasya SANTIKA, YULI Saputri, Gita Aprinda Sari, Raras Kartika Sena, Fikih Yuhada Setiawansyah Setiawansyah Sigit Priyanta 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