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All Journal Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Indonesian Journal of Artificial Intelligence and Data Mining Jurnal Sisfokom (Sistem Informasi dan Komputer) Jurnal Teknologi Sistem Informasi dan Aplikasi SPIRIT JOURNAL OF INFORMATION SYSTEM MANAGEMENT (JOISM) Journal of Advanced in Information and Industrial Technology (JAIIT) Teknika DECODE: Jurnal Pendidikan Teknologi Informasi JITSI : Jurnal Ilmiah Teknologi Sistem Informasi 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
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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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ANALISIS SENTIMEN LAYANAN APLIKASI MYTELKOMSEL MENGGUNAKAN METODE K-NEAREST NEIGHBORS Nugraha, Azariz Ananta Leo; Kacung, Slamet
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 7 No 2 (2025): EDISI 24
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v7i2.5876

Abstract

Aplikasi MyTelkomsel merupakan layanan digital yang digunakan untuk mengakses berbagai fitur Telkomsel, seperti pembelian paket data dan pengecekan pulsa. Namun, banyak ulasan pengguna menunjukkan ketidakpuasan terhadap performa aplikasi tersebut. Penelitian ini bertujuan untuk menganalisis sentimen pengguna terhadap layanan aplikasi MyTelkomsel menggunakan metode klasifikasi K-Nearest Neighbors (K-NN). Data dikumpulkan dari komentar pengguna di Google PlayStore dan Instagram, berjumlah 1.678 data. Proses analisis dimulai dari tahap preprocessing hingga klasifikasi dengan tiga jenis pengukuran jarak, yaitu Cosine Similarity, Euclidean Distance, dan Manhattan Distance. Hasil evaluasi menunjukkan bahwa metrik Cosine menghasilkan akurasi tertinggi sebesar 85%, sementara Euclidean dan Manhattan masing-masing menghasilkan 68% dan 57%. Penelitian ini menyimpulkan bahwa metrik Cosine lebih sesuai digunakan dalam klasifikasi sentimen berbasis teks dengan representasi TF-IDF.
Comparative Analysis of SVM and NB Algorithms in Evaluating Public Sentiment on Supreme Court Rulings Maulidiana, Putri Dwi Rahayu; Vitianingsih, Anik Vega; Kacung, Slamet; Maukar, Anastasia Lidya; Hermansyah, David
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 2 (2024): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i2.2116

Abstract

The legal events that happened to Ferdy Sambo and the Supreme Court’s decision in the cassation triggered emotional reactions and various opinions among the public, especially on social media sites such as Xapps. Some comments reflect people’s concerns about fairness in the legal system. They doubted the integrity of legal institutions or believed that decisions were unfair or in line with vested interests. This research aims to analyze public perceptions of Supreme Court decisions. The research process includes data collection, preprocessing, labeling, weighting, classification using Support Vector Machine and Naïve Bayes, and performance evaluation using a confusion matrix. A dataset of 624 was taken from X apps using the Twitter scraping technique. The lexicon method is used for data labeling, dividing the data into positive, negative, and neutral classes. The analysis results show 46 tweets categorized as positive sentiment, 133 tweets categorized as negative sentiment, and 422 tweets categorized as neutral sentiment. Based on testing with a data ratio of 80:20, both SVM and NB methods show good performance. The SVM criteria showed an accuracy of 0.84, precision of 0.61, recall of 0.78, and f1-score of 0.66, while the NB criteria showed an accuracy of 0.73, precision of 0.37, recall of 0.57, and f1-score of 0.35.
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 (IJCS)
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 Hariyanti, Yenny; Kacung, Slamet; Santoso, Budi
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
Comparative Analysis of Support Vector Regression and Linear Regression Models to Predict Apple Inc. Share Prices Pangestu, Resza Adistya; Vitianingsih, Anik Vega; Kacung, Slamet; Maukar, Anastasia Lidya; Noertjahyana, Agustinus
Indonesian Journal of Artificial Intelligence and Data Mining Vol 7, No 1 (2024): March 2024
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v7i1.28594

Abstract

Stock price prediction is a complex and important challenge for stock market participants. The difficulty of predicting stock prices is a major problem that requires an approach method in obtaining stock price predictions. This research proposes using machine learning with the Support Vector Regression (SVR) model and linear regression for stock price prediction—the dataset used in the daily Apple Inc historical data from 2018 to 2023. The hyperparameter tuning technique uses the Grid Search method with a value of k = 5, which will be tested on the SVR and Linear Regression methods to get the best prediction model based on the number of cost, epsilon, kernel, and intercept fit parameters. The test results show that the linear regression model with all hyperparameters k = 5 with the average taken performs best with a True intercept fit value. The resulting model can get an excellent error value, namely the RMSE value of 0.931231 and MSE of 0.879372. This finding confirms that the linear regression model in this configuration is a good choice for predicting stock prices.
Deteksi Notifikasi Suspend pada Aplikasi Ojek Online Menggunakan Metode MOORA Wijiono, Aditya Kusuma; Vitianingsih, Anik Vega; Kacung, Slamet; Maukar, Anastasia Lidya; Pamudi, Pamudi
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 7 No. 3 (2024): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jtsi.v7i3.42159

Abstract

Online motorcycle taxi drivers often face the risk of account suspension due to violations of rules that are not always clear or understood by them. This ignorance can cause drivers to be unaware of actions that can lead to suspension, which can impact their income and reputation. To overcome this problem, this study proposes the use of the Multi-Objective Optimization based on Ratio Analysis (MOORA) method in detecting and providing early notification regarding potential suspension. The MOORA method is used to analyze various parameters related to violations, such as the frequency and type of violations, as well as the number of accumulated violation points. By processing this data, the developed system can predict the possibility of suspension and provide notification to the driver. The results of the application of the MOORA method show that this system is effective in providing accurate notifications and can help drivers avoid actions that have the potential to cause suspension. The application of this system has the potential to reduce the number of suspension cases and increase driver awareness of actions that must be avoided.
MODEL SYSTEM USABILITY SCALE UNTUK EVALUASI KEPUASAN LAYANAN PROGRAM STUDI kacung, slamet; Umam, Khoirul; Sumirat, Lambang Probo
SPIRIT Vol 16, No 1 (2024): SPIRIT
Publisher : LPPM ITB Yadika Pasuruan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53567/spirit.v16i1.326

Abstract

Model Sus Untuk Evaluasi Kepuasan Layanan Program Studi di Perguruan Tinggi (PT). Penelitian ini bertujuan untuk memberikan kemudahan kepada mahasiswa, tenaga pendidik, dan tenaga kependidikan, serta mitra kerjasama dalam memberikan penilaian kepuasan kepada program studi karena dalam pengisian kusioner dapat melalui sistem yang dapat diakses kapan saja dan dimana saja selama terhubung dengan internet. Jenis penelitian ini menggunakan penelitian kualitatif dengan menggunakan metode System Usability Scale (SUS), penelitian ini digunakan untuk mengukur tingkat kepuasan pengguna sistem informasi menurut sudut pandang subyektif penggunannya. Hasil penelitian menemukan bahwa sistem kepuasan adalah sistem informasi yang digunakan untuk membantu kerja program studi dalam mengetahui kepuasan terhadap layanan yang dierikan. Ditinjau dari penggunanya sistem kepuasan ini belum dilakukan pengukuran kepuasan dari pengguna. hasil analisis sistem menggunakan metode System Usability Scale (SUS) dengan jumlah sampel 13 responden diperoleh nilai rata-rata 85. dengan kriteria penilaian pada Adjective rating adalah Good, dengan Grade Scale nilai A-. Adapun Acceptability Ranges dengan nilai Acceptable, yang artinya sistem tersebut dapat diterima dan digunakan oleh seluruh pengguna. Kata kunci: Sistem Informasi, Kepuasan Layanan, Metode System Usually Scale (SUS)
Design and Development of an Online Analytical Processing (OLAP) Application for Customer Profiling Analysis of Insurance "X" Kesuma Wardanie, Debleng Puja; Kacung, Slamet; Fauzi, Chamdan; Pamudi, Pamudi
Knowbase : International Journal of Knowledge in Database Vol. 4 No. 2 (2024): December 2024
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v4i2.8799

Abstract

The system's slow and inflexible response time is a characteristic of analytical processes based on transactional databases (OLTP), as experienced by PT Asuransi "X." This limitation arises because transactional databases are not designed for OLAP, which can provide various functions to perform synthesis and analysis that improve response time. This study aims to design and develop an Online Analytical Processing (OLAP) application to be used for customer profiling analysis at insurance company "X." In the insurance industry, effective and efficient data analysis is essential to understand customer behavior, perform segmentation, and make more informed decisions in marketing insurance products. The OLAP application developed in this study integrates various customer data dimensions, such as demographics, claim history, and owned products, facilitating multidimensional analysis for its users. The application design process includes system design, data collection, OLAP technology implementation, and application testing. The study results indicate that the application reveals that the majority of customers are male (56%), aged between 30 and 45 years (45%), and employed in the private sector. Additionally, in the city of Surabaya, there is a higher tendency to purchase the Mitra Sakinah life insurance policy. This information enables the company to better understand customer demographic characteristics and tailor its marketing strategies accordingly.
Analysis of A Priori Algorithm in Medical Data for Heart Disease Identification with Association Rule Mining Sutejo, Davip; Yudha Adhi Jaya, Villa Indra; Kacung, Slamet
Knowbase : International Journal of Knowledge in Database Vol. 4 No. 2 (2024): December 2024
Publisher : Universitas Islam Negeri Sjech M. Djamil Djambek Bukittinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30983/knowbase.v4i2.8909

Abstract

Heart disease is one of the leading causes of death worldwide, so it is important to identify risk factors that can contribute to the development of this disease in order to carry out early prevention. This study aims to identify patterns of association between risk variables and the incidence of heart disease using the Association Rule Mining (ARM) method combined with the A priori algorithm. The data used in this study includes lifestyle information, medical history, and other health parameters, obtained from the UCI Machine Learning repository. The analysis results showed that with a support value between 30% and 70%, the strongest association rule was found between sex (sex = 1) and angina (exang = 1), with a lift value of 1.67, indicating a strong positive relationship towards a positive diagnosis (target = 1). In addition, other moderate association rules were found, such as the combination of cp_1 = 1 and ca_0 = 1, with a lift value of about 0.73, indicating a weaker association. These findings suggest that some attribute combinations have higher predictive power, which can be used to improve prediction models in the medical diagnosis of heart disease. This research also highlights the main challenges faced by the A priori algorithm, such as computational complexity and selecting the right threshold to obtain significant rules
Co-Authors Achmad Muzakki Agustinus Noertjahyana Anastasia Lidya Maukar Anik Vega Vitianingsih Damayanti, Erika Dwi Budi Santoso DWI CAHYONO Fadilla, Muhammad Myrza Fardhan Maulana, Abelardi Fauzi, Ariq Ammar Fauzi, Chamdan Fitri Ana Wati, Seftin Hariyanti, Yenny Hengki Suhartoyo, Hengki Hermansyah, David Kesuma Wardanie, Debleng Puja Khoirul Umam Lambang Probo Sumirat, Lambang Probo Larisa, Damasya Ine Lidya Maukar, Anastasia Maulidiana, Putri Dwi Rahayu Muhammad Nasrullah Nugraha, Azariz Ananta Leo Pamudi Pamudi, Pamudi Pangestu, Resza Adistya Poetra, Akbar Rizki Prasetyo, Prasetyo Tri Utomo Pratama, Ferdian Rizki Pujiono, Halim Putri, Natasya Kurnia Sutejo, Davip Warsito Sujatmiko, Achmad Wati, Seftin Fiti Ana Wati, Seftin Fitri Ana Wijiono, Aditya Kusuma Yasin, Verdi Yudha Adhi Jaya, Villa Indra