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All Journal TEKNIK INFORMATIKA Sainteks Jurnal Pseudocode SMATIKA Jurnal Ilmiah KOMPUTASI Jurnal Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA InComTech: Jurnal Telekomunikasi dan Komputer Jurnal SOLMA Prosiding Seminar Nasional Teknoka Dinamisia: Jurnal Pengabdian Kepada Masyarakat SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Sisfokom (Sistem Informasi dan Komputer) IJID (International Journal on Informatics for Development) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Linguistik Komputasional Jurnal ICT : Information Communication & Technology Building of Informatics, Technology and Science FIKROH: JURNAL PEMIKIRAN DAN PENDIDIKAN ISLAM Jurnal Teknologi Dan Sistem Informasi Bisnis Zonasi: Jurnal Sistem Informasi Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Asiimetrik: Jurnal Ilmiah Rekayasa Dan Inovasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Teknosains : Jurnal Sains,Teknologi dan Informatika Infotech: Journal of Technology Information Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) KLIK: Kajian Ilmiah Informatika dan Komputer Sibatik Journal : Jurnal Ilmiah Bidang Sosial, Ekonomi, Budaya, Teknologi, Dan Pendidikan Jurnal Penyuuhan dan Pemberdayaan Masyarakat (JPPM) STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer SmartComp Fikroh: Jurnal Pemikiran dan Pendidikan Islam JURNAL TEKNIK INFORMATIKA DAN KOMPUTER Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Cosmic Jurnal Teknik BHAKTI JIVANA Smatika Jurnal : STIKI Informatika Jurnal
Firman Noor Hasan
Universitas Muhammadiyah Prof. Dr. Hamka
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Analisis Sentiment Ulasan Aplikasi Riliv di Google Playstore dengan Algoritma SVM Andriani, Vivi; Hasan, Firman Noor
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.7860

Abstract

This study aims to conduct sentiment analysis on user reviews of the Riliv: Mental Health App on Google Play Store using the Support Vector Machine (SVM) algorithm. The analysis process includes review data collection via web scraping, text cleaning using text preprocessing, automatic labeling based on rating scores, data transformation using the TF-IDF method, data splitting with Stratified K-Fold Cross Validation, SVM model training, and performance evaluation. The dataset comprises 2,000 reviews with an imbalanced label distribution: positive (75,3%), netral (5,3%), and negative (19,4%). The classification results show that the SVM model achieved an accuracy of 85.56%. It performed well in identifying positive sentiment with an f1-score of 0.96 and negative sentiment with 0.69. However, the model failed to classify neutral sentiment due to the small number of data, which was insufficient for meaningful pattern recognition. Evaluation and visualization results indicate that label imbalance is a major challenge. Therefore, additional strategies such as data balancing, class weighting, or the use of alternative algorithms are necessary. This research is expected to serve as a foundation for developing a more accurate and fair sentiment analysis system across all sentiment categories in the context of digital mental health services.
Penerapan Naïve Bayes untuk Mengklasifikasikan Sentimen Tidak Seimbang pada Ulasan Aplikasi Berbasis Etika Konsumen Lingga, Lingga; Hasan, Firman Noor
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.7867

Abstract

This study aims to classify user sentiment toward an ethics-based consumption application using the Multinomial Naïve Bayes algorithm. The application examined contains social and moral content, often provoking complex opinion expressions. A total of 2,000 user reviews were collected from Google Play Store using web scraping and processed through a series of text preprocessing steps: case folding, cleansing, tokenizing, stopword removal, and stemming. The data were converted into numerical form using the Term Frequency–Inverse Document Frequency (TF-IDF) method and labeled into three sentiment categories: positive, neutral, and negative. The evaluation results show that the model achieved a precision of 92%, recall of 100%, and an f1-score of 96% for positive sentiment. However, the model underperformed in recognizing neutral and negative sentiments due to class imbalance. This study contributes to understanding the limitations of probabilistic classification models in handling imbalanced public opinion in socially driven digital spaces.
Analisis Sentimen Pengguna Aplikasi CapCut Pada Ulasan di Play Store Menggunakan Metode Naïve Bayes Meliyawati; Hasan, Firman Noor
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1555

Abstract

There is an increasing interest in sharing experiences displayed in video visualizations, creating a demand for efficient and simple editing tools. CapCut is an all-in-one creative digital platform that enables video editing on browser, desktop and mobile. The CapCut app is one of the most downloaded apps on the Play Store with 500 million downloads and is available for free. CapCut app is perfect for beginner editors as it has a simple interface with various interesting features such as templates that are easy to operate without the need for additional software. However, this cannot guarantee the satisfaction of its users. Various experiences that are felt affect the assessment given by users. Sentiment analysis is important to determine the level of user satisfaction, the results of which can be used as a reference for improving the quality of the application. To find out user reviews of the CapCut application, sentiment analysis is carried out using the Naïve Bayes method with the aim of knowing the number of positive and negative sentiments from user reviews. The data used is taken from the review column available on the Play Store using web scrapping techniques with the help of Google Colab as much as 880 user review data. The data is divided into 60% training data which is 528 reviews and 40% test data which is 352 reviews. The analysis resulted in 30 more negative sentiments than positive sentiments with the number of negative sentiments totaling 455 reviews and the number of positive sentiments totaling 425 reviews. Based on the evaluation using confusion matrix, the accuracy result is 84.09%, precision is 91.91%, and recall is 73.53%.
Analisis Sentimen Terhadap KPU 2024 Berdasarkan Tweet Media Sosial Twitter Menggunakan Algoritma Naïve Bayes Dion Parisda Ray; Firman Noor Hasan; Ahmad Rizal Dzikrillah
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1587

Abstract

The development of technology is currently very rapid making the dissemination of information faster, the dissemination of information is very easy to get on social media such as Twitter. Twitter social media itself provides features for its users to be able to send and read information in the form of text or video. Elections are a very important moment for the Indonesian people in choosing leaders, in this case the "2024 KPU" as the organizer is expected to be able to run the elections so that they run well. Twitter data collected with the keyword "KPU 2024" obtained a total of 3057 datasets, followed by a cleansing process which produced 715 datasets. The aim of this research is to find out how many positive and negative tweets comments and to indicate the accuracy of the implementation of the Naïve Bayes method. The accuracy results given by the Naïve Bayes algorithm are 67.13% with a precision of 66.04% and a recall of 100.00%. This research was conducted to see public sentiment towards the "2024 KPU" later. Evaluation results in the confusion matrix obtained true positives of 457 and true negatives of 235
ANALISIS SENTIMEN MASYARAKAT TERHADAP PAYLATER MENGGUNAKAN METODE NAIVE BAYES CLASSIFIER Alfandi Safira; Hasan, Firman Noor
ZONAsi: Jurnal Sistem Informasi Vol. 5 No. 1 (2023): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode Januari 2023
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v5i1.12856

Abstract

Online shopping is so popular with the public because it is easy and convenient to do. The convenience of online shopping is supported by the payment method via paylater. However, paylater also results in bad behavior such as impulse buying. Various responses from the community made researchers conduct research to find out the public's view of paylater. In this study, researchers tried to do sentiment analysis using the Naive Bayes Classifier and TextBlob methods from the TetxBlob library with the Python programming language. From the dataset collected via Twitter, it produces 405 data. Sentiment analysis using the Naive Bayes Classifier method produces a negative sentiment of 70.62% or 286 data, positive sentiment is 22.72% or 92 data, neutral sentiment is 6.67% or 27 data. Meanwhile, using the TextBlob method also produced more negative sentiment, namely 55.8% or 226 data, positive sentiment collected 33.09% or 134 data, neutral sentiment amounted to 11.11% or 45 data. Thus, it can be concluded that the community feels unfavorable towards the use of paylater. In testing the model with the confussion matrix, it can be seen that the Naive Bayes Classifier algorithm is more accurate by 91% compared to TextBlob which is only 61%.
Pendalaman Kompetensi Keahlian Kejuruan Teknik Permesinan Kepada Siswa SMKN 1 Cikarang Pusat Ariyansah, Riyan; Hasan, Firman Noor; Ramzah, Harry; Mugisidi, Dan; Sinduningrum, Estu; Rahmatullah, Ahmad Faiz
Jurnal Pengabdian kepada Masyarakat Nusantara Vol. 4 No. 4 (2023): Jurnal Pengabdian kepada Masyarakat Nusantara (JPkMN)
Publisher : Lembaga Dongan Dosen

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Tujuan pengabdian masyarakat ini ialah untuk mendalami kompetensi keahlian kejuruan teknik permesinan di SMKN 1 Cikarang Pusat melalui program pendalaman kompetensi. Studi ini melibatkan 30 siswa jurusan Teknik Permesinan dalam penerapan pendekatan penelitian pengabdian masyarakat. Identifikasi masalah dilakukan melalui survei dan wawancara awal, yang mengarah pada perancangan rancangan pendalaman kompetensi. Pelaksanaan program melibatkan studi literatur, penerapan rencana pendalaman kompetensi, dan pengumpulan data melalui observasi serta tes pemahaman siswa. Hasil menunjukkan peningkatan signifikan dalam pemahaman siswa, keterlibatan aktif, pengaruh positif keterlibatan industri, dan peningkatan skill praktis sebanyak 20%. Program ini juga meningkatkan keselarasan kurikulum dengan kebutuhan industri, mempersiapkan siswa untuk tantangan di dunia kerja. Temuan ini memberikan kontribusi pada pemahaman lebih lanjut tentang implementasi pendalaman kompetensi dalam pendidikan kejuruan.
ANALYSIS OF PUBLIC SENTIMENT ON GOOGLE PLAY STORE TIJE APPLICATION USERS USING NAÏVE BAYES CLASSIFIER METHOD Sari, Laila Atikah; Ramadhita, Nindia Fitri; Hasan, Firman Noor
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1648

Abstract

Advances in information technology have an influence on companies and agencies to innovate. The Tije application is one of the innovations that has been made by PT Tranportasi Jakarta which is used by its users. However, each application has advantages and disadvantages, including the Tije application which has an impact on the disruption of the function of supporting user services as the purpose of making this application. This can certainly trigger a response from users which can be submitted through the review column on the Google Play Store platform. This research was conducted to analyze the sentiment of community reviews of Tije application users on the Google Play Store platform using the Naïve Bayes Classifier method. Tije application review data collection is done by web scrapping techniques on the Google Play Store using Google Colab. Then, the collected data will be processed to eliminate inappropriate elements and get sentiment content on each review, whether the review falls into the category of positive or negative sentiment towards the Tije application. The results of this study conclude that users are dissatisfied and disappointed with the services available on the Tije application. This is evidenced by the number of negative sentiments that are more dominant and in the application of the Naive Bayes algorithm in this study, obtained quite good accuracy results of 85.88%.
SENTIMENT ANALYSIS OF CUSTOMER SATISFACTION IN GOJEK AND GRAB APPLICATION REVIEWS USING THE NAIVE BAYES ALGORITHM Ananda, Ridha Faiz; Syahri, Alfi; Hasan, Firman Noor
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1680

Abstract

Online motorcycle taxis are a widely favored mode of public transportation in Indonesia. There are several companies providing online motorcycle taxi services in Indonesia, with Gojek and Grab dominating the market. In this rapidly digitizing era, social media has become a platform for Indonesian citizens to express their evaluations and opinions. One common platform used by users to express their evaluations is the Google Play Store, where users can provide ratings and opinions on the applications they use, including users of Gojek and Grab applications.This research aims to understand and analyze the sentiments of the public towards the two dominant giants in the online motorcycle taxi market in Indonesia based on review data from the Google Play Store using the Naive Bayes algorithm. The data used consists of user reviews from May 14, 2023, to July 26, 2023, totaling 300 data points for each application. This data will undergo pre-processing to remove irrelevant elements. The Naive Bayes algorithm is used to classify the existing sentiments into two classes: positive and negative.The results of this research conclude that Gojek users give positive reviews at 49% and negative reviews at 51%, which include praises for the drivers and services provided by the company, complaints about the heaviness of the application, and some disruptions in the Gopay payment method. Meanwhile, Grab users give positive reviews at 67% and negative reviews at 33%, which include customer satisfaction with attractive promos, complaints about the heaviness of the application after the latest update, and the high cost of Grabexpress and Grabfood services.
Analisis Sentimen Masyarakat Terhadap Fenomena Childfree (Kehidupan Tanpa Anak) Pada Twitter Menggunakan Algoritma Naïve Bayes Erizal, Erizal; Hasan, Firman Noor
Journal of Information System Research (JOSH) Vol 5 No 3 (2024): April 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v5i3.5064

Abstract

Childfree is a phenomenon that occurs not only in the world but also in Indonesia. There are many negative and positive stigmas that arise regarding the phenomenon of living a life without children, especially in urban areas. The public's response to the childfree phenomenon, especially in urban areas in Indonesia, is varied, and is more influenced by various factors. In this research, we analyzed netizens' views on the childfree phenomenon using the Naïve Bayes method assisted by rapidminer tools to process text data collected via social media X. The aim of this research is to analyze and present data regarding public sentiment towards the childfree phenomenon in Indonesia. The results of the research found that 319 referred to negative sentiment, and only 181 referred to positive sentiment. The accuracy results produced by the Naïve Bayes algorithm were 95.02%. Showing that the childfree phenomenon is chosen by some netizens, especially in urban areas, because they want young people to focus on education and careers in order to make their lives more stable.
Implementation of Data Mining to Predict Student Study Period with Decision Tree Algorithm (C4.5) Putri, Kirana Alyssa; Febriawan, Dimas; Hasan, Firman Noor
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 13 No. 1 (2024): MARET
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v13i1.1943

Abstract

Graduating on time is what every student wants to accomplish in college. Students of Prof. Dr. Hamka Muhammadiyah University are one of those who have this dream. Based on 2020 graduates data from the Tracer Study, 60% said the university had a high enough impact  on improving competence.  This data indicates that university needs to evaluate improvement of academic quality. Often, students have difficulty finding information about important factors that support achieving timely graduation. A prediction analysis is needed to provide information about the student's graduation study period. For this analysis, data mining is implemented using the classification function of the decision tree (C4.5) algorithm with RapidMiner tools. The methodology for implementing data mining follows the stages of Knowledge Discovery In Database (KDD), beginning with data collection, preprocessing, transformation, data mining, and evaluation. The research findings consist of visualization and decision tree rules that reveal GPA as the most influential factor in determining a student's study period.There is other information, namely, students graduated on time (less than equal to 4 years) amounted to 170 or 54.5% and students did not graduate on time (more than 4 years) amounted to 142 or 45.6%. Testing the performance of decision tree (C4.5) utilizing confusion matrix through RapidMiner tools, resulted in accuracy reaching 83.87%, with precision of 87.50% and recall of 91.18%. Provides evidence that the decision tree algorithm (C4.5) has optimal performance to provide valuable information about predicting student graduation in order to increase student enrollment with the right study period.
Co-Authors Abdillah, Allif Rizki Abdul Syakir Achmad Ramadhan Achmad Sufyan Aziz Afandi, Irfan Ricky Affandi, Irfan Ricky Afikah, Prista Afnan Sabili, Dian Ainurrafik Agus Fikri Agus Fikri Ahmad Rizal Dzikrillah Ahmad Rizal Dzikrillah Ahmad Roshid Ahmad Syahril Ahmad Syahril Al Ghozi, Dhiyauddin Alfandi Safira Alim, Endy Sjaiful Allif Rizki Abdillah Allif Rizki Abdillah Allif Rizki Abdillah Ammar Rusydi Ananda Prasta Warasati Janah Ananda, Ridha Faiz Andika Saputra Andriani, Vivi Anhari, Tirta Anwar Hidayat Ari Wibowo Arief Wibowo Arien Bianingrum Rossianiz Arvin Rafialdo Aulia, Muhammad Fathan Avorizano, Arry Azhar Haikal Anwar Azhar Haikal Anwar Azis Styo Nugroho Bagas Kembar Rezkyllah Bahrul Rozak Bahrul Rozak Dan Mugisidi Dandie Triyanto Desty Afni Dewi Mayangsari Dian Ainurrafik Afnan Sabili Dian Ainurrafik Afnan Sabili Diana Fitri Lessy Diana Fitri Lessy Dimas Febriawan Dimas Febriawan Dimas Febriawan Dion Parisda Ray Djeli Moh Yusuf Doni Gunawan Rambe E Erizal Erizal Erizal Estu Sinduningrum Estu Sinduningrum Fachri Zaini Fadli Al Gani Fadli Hardiyanto Putra Fadli, Khairul Faisal Parsakh Nursyamsi Faisal Parsakh Nursyamsyi Fajar Sidik Faldy Irwiensyah Faldy Irwiensyah Faldy Irwiensyah, Faldy Farhan Bias Purnama Putra Farhan Nufairi Farhan Nufairi Fathurrohman, Sewin Fauzan Setya Ananto Fauzi Kurniawan Fayakun Kun Febriandirza, Arafat Febriawan, Dimas Gusnul Mahesa Hafizh Dhery Al Assyam Handika, Yusuf Hanif, Isa Faqihuddin Hardyatman, Intan Diah Hazbi Santoso Hibatullah Faisal Hibatullah Faisal Hibatullah Faisal Hilmi Ammar Hilmy Zhafran Muflih I Ketut Sudaryana, I Ketut Ibnu Suhada Indra Ramadhan Indra Ramadhan Indriyanti, Prastika Intania Widyaningrum Irawati Irawati Irfan Ricky Afandi Irfan Ricky Affandi Irma Wahyuningtyas Isnan Wisnu Prastiyo Kamayani, Mia kivandi Nugroho Krisna, Mohammad Dito Dwi Kurniyati Nur Lathifah Dini Rachmawati Lingga Lingga Lingga Lita Astri Pramesti Luqman Abdur Rahman Malik Lutfi Triyuli Evana Rizki Luthfi Akbar Ramadhan M. Asep Rizkiawan Meliyawati MILASARI, LISA ASTRIA Mohammad Akhdaan Juliandra Muchammad Sholeh Muchammad Sholeh Muflih, Hilmy Zhafran Muhamad Saiful Arif Muhammad Abid Fajar Muhammad Ardhi Ryan Saputra Muhammad Ghiffar Sistani Muhammad Ikhwan Muhammad Ikhwan Muhammad Rafly Al Fattah Zain Muhammad Ridwan Muhammad Rifansyah Mukti, Avis Tantra Mutiara Zahra Arifin Nisa Qonita Rizkina Nofendri, Yos Nugroho, Dendy Aprilianto Nunik Pratiwi Oktarina Heriyani Pamungkas, Dimas Panji Islami Anakku Pavita, Rachma Pranata, Ananda Bagas Prisilia Talakua Prista Afikah Purnamaningsih, Ine Rahayu Putri, Kirana Alyssa Rafli Erlangga Rahman Malik, Luqman Abdur Rahmatullah, Ahmad Faiz Ramadhita, Nindia Fitri Ramzah, Harry Reisa Inayah Rian gustini Ridwan Bagus Andreyanto Ridwan Maulana Subekti Rika Nurhayati Riyan Ariyansah Rizki Alamsyah Rizki Kamelia Rizky Ramdhani Rosalina Rosalina Rozak, Bahrul Saputra, Ramadani Sari, Jessica Windi Sari, Laila Atikah Setiawan, Ahmat Sewin Fathurrohman Simamora, Silvia Damayanti Sinduningrum, Estu Sistani, Muhammad Ghiffar Siti Nurhaliza Sri Fitriani Sunata, Muhamad Hafidz Ardian Syahri, Alfi Tasya Rizki Salsabilla Tia Anggita Sari Transiska, Dwi Wahyu Stiyawan Wahyuningtyas, Irma Wanda Aulia Widyastuti Andriyani Windi Al Azmi Wulandari, Sania Zahra, Khofifah Humaeroh Az Zaini, Fachri Zuhri Halim Zuhri Halim, Zuhri