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All Journal Jurnal Teknologi dan Manajemen Informatika Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Indonesian Journal of Artificial Intelligence and Data Mining JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Journal of Advanced in Information and Industrial Technology (JAIIT) Teknika Bulletin of Computer Science Research Decode: Jurnal Pendidikan Teknologi Informasi JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Informatics, Electrical and Electronics Engineering Jurnal Informatika Teknologi dan Sains (Jinteks) Knowbase : International Journal of Knowledge in Database The Indonesian Journal of Computer Science Multidisciplinary Indonesian Center Journal
Slamet Kacung, Slamet
Teknik Informatika Universitas Dr. Seotomo
Religion Aerospace Engineering Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Other

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Sistem Pakar Diagnosa Organisme Pengganggu Tanaman (OPT) Semangka Menggunakan Metode Fuzzy Tsukamoto Wistu Ari Wibowo; Anik Vega Vitianingsih; Yudi Kristyawan; Slamet Kacung
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3653

Abstract

Diagnosa yang tepat sangat penting untuk mengendalikan dan mencegah penyebaran organisme pengganggu tanaman semangka. Namun, seringkali petani kesulitan dalam mendiagnosa organisme pengganggu tanaman yang menyerang semangka. Masalah yang dihadapi adalah kurangnya pengetahuan dan pengalaman petani dalam mengetahui jenis organisme pengganggu semangka. Hal ini dapat mengakibatkan kesalahan dalam diagnosa dan pengobatan yang tidak tepat dapat mengurangi produksi dan kualitas buah yang dihasilkan. Tujuan penelitihan ini adalah membuat sistem pakar untuk identifikasi jenis organisme pengganggu tanaman semangka menggunakan metode Fuzzy Tsukamoto berdasarkan parameter organisme pengganggu tanaman semangka yang menyerang daun, batang dan kulit buah semangka. Metode tersebut digunakan karena mudah diterapkan dan bisa menghasilkan keputusan dengan masukan data yang samar. Variabel keluaran adalah kondisi besar serangan hama dan penyakit tanaman semangka yang dikelompokkan menjadi empat kategori yaitu ringan, sedang, berat dan puso.
Sentiment Analysis on the FIFA U-20 World Cup in Argentina Using Support Vector Machine Warsito Sujatmiko, Achmad; Vitianingsih, Anik Vega; Kacung, Slamet; Cahyono, Dwi; Lidya Maukar, Anastasia
The Indonesian Journal of Computer Science Vol. 13 No. 3 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i3.3973

Abstract

The decision made by FIFA regarding the selection of the soundtrack and the host country for the FIFA U-20 World Cup has sparked emotional reactions among the public and raised concerns about the event, especially on social media platform X. This is due to FIFA’s decision to choose a soundtrack not from the host country, Argentina, but from the previous host, Indonesia. FIFA should advocate for the creation of a soundtrack by the host country to reflect its distinctive characteristics or atmosphere. Concerns about the U-20 World Cup in Argentina have also been fueled by the country’s economic crisis, which is feared to affect the facilities and infrastructure for the young players representing their nations. This research focuses on filtering public responses to FIFA’s decisions regarding the soundtrack selection and the host country for the U-20 World Cup into positive, neutral, and negative categories using the Support Vector Machine (SVM) method. The research aims to provide policy recommendations regarding the host selection process and cultural representation in international sports events. Additionally, this study is expected to provide a deeper understanding of the preferences and values held by the public regarding international sports. The research steps include data collection, pre-processing, labeling, weighting, and classification using a Support Vector Machine. The data for this research were obtained through crawling on social media platform X, totaling 2400 data points. The performance evaluation of the SVM algorithm using a 50:50 ratio of training and testing data yielded an average accuracy of 85.71%, Precision of 85.98%, Recall of 85.71%, and F1-score of 85.58%.
ANALISIS SENTIMEN TERHADAP PUTUSAN MAHKAMAH KONSTITUSI TENTANG BATASAN UMUR CAPRES DAN CAWAPRES MENGGUNAKAN METODE NAÏVE BAYES Yenny Hariyanti; Slamet Kacung; Budi Santoso
Multidisciplinary Indonesian Center Journal (MICJO) Vol. 1 No. 1 (2024): Vol. 1 No. 1 Edisi Januari 2024
Publisher : PT. Jurnal Center Indonesia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62567/micjo.v1i1.61

Abstract

Penelitian ini mengkaji reaksi publik terhadap keputusan Mahkamah Konstitusi (MK) Indonesia yang mempertahankan batasan umur minimal 35 tahun untuk calon presiden dan wakil presiden. Dengan menggunakan metode Naïve Bayes untuk menganalisis sentimen dari data Twitter, penelitian ini bertujuan untuk mengungkap persepsi publik terhadap regulasi ini. Analisis menunjukkan mayoritas sentimen negatif (90.9%), dengan hanya 6.6% sentimen positif dan 2.5% sentimen netral, menandakan ketidakpuasan yang dominan di kalangan publik. Akurasi analisis sentimen yang dihasilkan mencapai 67.98%, menegaskan efektivitas Naïve Bayes dalam konteks ini. Penelitian menghasilkann betapa pentingnya akan pembahasan lebih mendalam mengenai syarat pencalonan yang dapat mencerminkan aspirasi masyarakat agar mempertimbangkan aspek pengalaman dan kedewasaan. Dalam konteks yang lebih luas, temuan ini memberikan wawasan berharga tentang dinamika opini publik dan potensi revisi peraturan terkait, merekomendasikan kajian lebih lanjut untuk memahami dampak kebijakan tersebut terhadap struktur demokrasi Indonesia
SISTEM INFORMASI INVENTORY PADA CV. SAMUDRA LAUTAN BERKAT PASURUAN MENGGUNAKAN METODE SCRUM Mochamad Ramadhani; Lambang Probo Sumirat; Slamet Kacung
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6259

Abstract

This study aims to design a web-based inventory information system at CV. Samudra Lautan Berkat Pasuruan to improve the efficiency and accuracy of stock management. The previous manual process using Microsoft Excel was considered ineffective and prone to errors. The system was developed using the Agile Scrum methodology which allows for structured and flexible development. This study uses a descriptive approach with data collection through observation, interviews, and literature studies. The system is built using PHP and MySQL, and applies the FIFO method for stock management. The features developed include recording incoming and outgoing goods, reports, and stock monitoring by admins, warehouses, and leaders. The implementation results show that this system accelerates the recording and reporting process, minimizes human error, and supports more accurate decision making in inventory management.
Sentiment Analysis of Online Lending Services Using Support Vector Machine and Logistic Regression Ardita Isnanda Rahayu; Slamet Kacung
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.574

Abstract

This research examines public sentiment toward online lending services in Indonesia by analyzing opinions from social media platforms, specifically YouTube and Twitter, collected from January 2021 to January 2024. The objective of this study is to develop an accurate sentiment classification system that can effectively categorize public opinions into positive, negative, and neutral sentiments, thereby providing valuable insights for regulatory bodies and service providers to understand consumer concerns and improve service quality. The collected data underwent thorough preprocessing, semi-automatic labeling, and Term Frequency-Inverse Document Frequency (TF-IDF) weighting. Four classification models were evaluated: Support Vector Machine (SVM) with Linear, Polynomial, and Radial Basis Function (RBF) kernels, and Logistic Regression. Results demonstrate that Linear SVM achieves the best performance with an accuracy of 90.17% and an F1-score of 0.902, effectively categorizing sentiments across all classes while excelling particularly in negative and neutral categories. The expected impact of this analysis is to provide evidence-based recommendations for policymakers in financial technology regulation and help online lending service providers understand consumer satisfaction levels to improve their service delivery. This study offers valuable insights for service providers and regulatory bodies seeking to better understand and address public concerns in this domain.
Sentiment Analysis On Tripadvisor Travel Agent Using Random Forest, Support Vector Machines, and Naïve Bayes Methods Ariq Ammar Fauzi; Anik Vega Vitianingsih; Slamet Kacung; Anastasia Lidya Maukar; Seftin Fiti Ana Wati
Teknika Vol. 14 No. 1 (2025): March 2025
Publisher : Center for Research and Community Service, Institut Informatika Indonesia (IKADO) Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34148/teknika.v14i1.1198

Abstract

TripAdvisor faces problems in improving the quality of service on its application, namely the presence of unexpected or non-functional features, which can affect the user experience and reduce trust in the application.  This research aims to develop an application capable of performing sentiment analysis on TripAdvisor application user reviews on the Google Play Store with negative, positive, and neutral classifications using the Random Forest (RF), Support Vector Machine (SVM), and Naïve Bayes (NB). The RF method was chosen in this study because of its ability to handle large and complex data very accurately, while SVM is able to classify data on a large scale and is resistant to overfitting, while NB is able to classify text with clear probabilities. The Lexicon-based method as data labelling. The results of sentiment analysis from 1500 reviews with web scrapping show the classification of positive, negative, and neutral sentiments of 48, 726, and 646 data, respectively. Model performance in RF, SVM, and NB testing gets an accuracy value of 94%, 93.6%, and 77.8%, respectively. The RF model produces the best accuracy compared to other methods. The RF model produces the best accuracy compared to other methods. The results of sentiment analysis from 1500 user reviews allow developers to identify features that are often criticized or do not function properly in their application services.
Sentiment Analysis Of NTB Syariah Bank Application Services using The Naïve Bayes and Support Vector Machine Methods Muh Nabil; Anik Vega Vitianingsih; Slamet Kacung; Anastasia Lidya Maukar; Seftin Fitri Ana Wati
Jurnal Teknologi dan Manajemen Informatika Vol. 11 No. 2 (2025): Desember 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v11i2.16311

Abstract

This research analyzed user sentiment toward the NTB Syariah application using Support Vector Machine (SVM) and Naïve Bayes classification methods. A dataset comprising 814 reviews was obtained via web scraping, with 245 allocated for testing. Preprocessing encompassed cleaning, case folding, tokenization, filtering, and stemming, while sentiment labeling employed a lexicon-based approach integrated with TF-IDF weighting, categorizing reviews as positive, neutral, or negative. Model performance was assessed through accuracy, precision, recall, and F1-score metrics. Results demonstrated SVM's superior performance (accuracy: 92.65%; precision: 0.9327; recall: 0.9265; F1-score: 0.9149) compared to Naïve Bayes (accuracy: 84.49%; precision: 0.8415; recall: 0.8449; F1-score: 0.8005). SVM exhibited greater robustness in managing high-dimensional, complex, and moderately imbalanced datasets, delivering consistent cross-class sentiment classification. Conversely, Naïve Bayes remained computationally efficient and suitable for rapid implementation scenarios. These findings underscore machine learning's efficacy in sentiment analysis for digital banking platforms.
Sentiment Analysis of Alfagift Application User Reviews Using Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) Methods Erika Damayanti; Anik Vega Vitianingsih; Slamet Kacung; Hengki Suhartoyo; Anastasia Lidya Maukar
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 4 No. 2: JULI 2024
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v4i2.478

Abstract

The rapid advancement of mobile apps has emerged as an important aspect of the routine of internet-connected users. In Indonesia, many companies are introducing their apps to improve the quality of service for users, and Alfamart is one of them. However, users have identified many shortcomings in these apps. This feedback is provided by users on the review feature of the Alfagift app on the Google Play Store. This research aims to apply a sentiment analysis approach to identify the application's shortcomings so that developers can understand the aspects that need to be improved to improve the quality of application services. The research stages include data collection, preprocessing, labeling, weighting, classification of LSTM and SVM methods, and performance evaluation using a confusion matrix. The dataset consists of 1000 reviews obtained through web scraping techniques. This research uses the Lexicon-based method to classify the dataset into positive, negative, and neutral categories. The analysis results show that 801 data are classified as positive sentiment, 77 as negative sentiment, and 122 as neutral sentiment. Based on testing, both SVM and LSTM methods show good performance. The best accuracy results were obtained using the SVM method, which amounted to 83.5%. Meanwhile, the LSTM method achieved an accuracy of 82%.
Co-Authors Achmad Muzakki Anastasia Lidya Maukar Anastasia Lidya Maukar Anastasia Lidya Maukar Anastasia Lidya Maukar Anik Vega Vitianingsih Anik Vega Vitianingsih Anik Vega Vitianingsih Anik Vega Vitianingsih Anik Vega Vitianingsih Anik Vega Vitianingsih Ardita Isnanda Rahayu Ariq Ammar Fauzi Budi Santoso Budi Santoso DWI CAHYONO Edi Prihartono Erika Damayanti Fardhan Maulana, Abelardi Fitri Ana Wati, Seftin Hengki Suhartoyo KRISTIAWAN KRISTIAWAN Lambang Probo Sumirat Larisa, Damasya Ine Lidya Maukar, Anastasia Mochamad Ramadhani Moh. Saiful Rohman Muh Nabil Muhammad Nasrullah Pamudi Pamudi, Pamudi Poetra, Akbar Rizki Prasetyo, Prasetyo Tri Utomo Pratama, Ferdian Rizki Pujiono, Halim Putri, Natasya Kurnia Rohmah, Farah Aqmarinar Seftin Fiti Ana Wati Seftin Fitri Ana Wati Selvi Amalia Dewi Sutejo, Davip Verdi Yasin Warsito Sujatmiko, Achmad Wati, Seftin Fitri Ana Wistu Ari Wibowo Yenny Hariyanti Yudha Adhi Jaya, Villa Indra Yudi Kristyawan