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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) SITEKIN: Jurnal Sains, Teknologi dan Industri JUSIFO : Jurnal Sistem Informasi Jurnal Ilmiah Rekayasa dan Manajemen Sistem Informasi Sistemasi: Jurnal Sistem Informasi JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA Seminar Nasional Teknologi Informasi Komunikasi dan Industri Jurnal Inotera JURIKOM (Jurnal Riset Komputer) Jurnal Sistem Cerdas Building of Informatics, Technology and Science Jurnal Sistem Informasi dan Informatika (SIMIKA) Indonesian Journal of Electrical Engineering and Computer Science JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Indonesian Journal of Data and Science Jurnal Teknik Informatika (JUTIF) KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Indonesian Journal of Informatic Research and Software Engineering Malcom: Indonesian Journal of Machine Learning and Computer Science CONSEN: Indonesian Journal of Community Services and Engagement Journal of Informatics, Electrical and Electronics Engineering The Indonesian Journal of Computer Science INOVTEK Polbeng - Seri Informatika Jurnal Ilmu Komputer dan Teknik Informatika Jurnal Pengabdian Masyarakat Berdampak
Inggih Permana
Universitas Islam Negeri Sultan Syarif Kasim Riau
Religion Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering 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 Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Social Sciences Other

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Klasifikasi Sentimen Pengguna X Terhadap Pemboikotan Produk Pro Israel Menggunakan Algoritma Machine Learning Susanti, Pingki Muliya; Afdal, M; Permana, Inggih; Marsal, Arif
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.v6i4.6533

Abstract

The campaign to boycott pro-Israel goods emerged as a result of the enduring conflict between Israel and Palestine. This boycott initiative led to a decline in sales, which adversely impacted the livelihoods of employees, manifesting in diminished bonuses, salary reductions, and job terminations. Such actions elicited a variety of reactions from the public on platform X. This study seeks to categorize the sentiments of X users regarding the boycott of pro-Israel products by comparing the efficacy of Machine Learning algorithms, namely Support Vector Machine and Random Forest. To address the class imbalance within the dataset, this research employs the synthetic minority over-sampling technique (SMOTE). The dataset comprised 2,275 entries, gathered through web scraping methods on the X platform. The findings indicate that a majority of X users in Indonesia endorse the boycott movement, exhibiting a positive sentiment of 58%. The SVM algorithm, when combined with SMOTE, demonstrated the highest performance in sentiment classification, achieving an accuracy of 90.54%, whereas Random Forest attained an accuracy of only 83.1%. This research offers insights into the views of the Indonesian populace regarding the boycott of pro-Israel products.
Analisis Sentimen Terhadap Program Makan Bergizi Gratis Menggunakan Algoritma Machine Learning Pada Sosial Media X Triningsih, Elsa; Afdal, M; Permana, Inggih; Rozanda, Nesdi Evrilyan
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.v6i4.6534

Abstract

The government has launched the Free Nutritious Meal Program as part of a strategic effort to reduce stunting in Indonesia. However, the program has generated a lot of controversy among the public, especially regarding the large budget allocation that is considered burdensome and its impact on the education sector and the country's financial stability. This study aims to analyze public sentiment towards the program by utilizing data from social media platform X (Twitter) as much as 2,400 data. Public sentiment is classified into three categories, namely positive, negative, and neutral, using two machine learning algorithms, namely Support Vector Machine (SVM) and Random Forest. In addition, the SMOTE technique is used to handle data imbalance in the model training process. The analysis results showed that negative sentiments dominated at 46%, with the main issue highlighted being the high budget allocation and its impact on education. In terms of performance, the SVM algorithm with SMOTE produced the highest accuracy of 85.74%, outperforming the Random Forest algorithm which only achieved 81.53% accuracy.
Perbandingan Kernel Algoritma Support Vector Regression Terhadap Performa Prediksi Produksi Kelapa Sawit: Comparison of the Support Vector Regression Kernel Algorithm on the Performance of Palm Production Prediction Maulana, Rizki Azli; Permana, Inggih; Salisah, Febi Nur; Ahsyar, Tengku Khairil; Jazman, Muhammad
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 1 (2025): MALCOM January 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i1.1410

Abstract

Produksi kelapa sawit merupakan salah satu faktor utama dalam industri perkebunan kelapa sawit yang memengaruhi kesejahteraan ekonomi suatu daerah. Dalam upaya untuk meningkatkan prediksi produksi kelapa sawit, algoritma Support Vector Regression (SVR) telah diadopsi sebagai metode prediksi yang potensial. Namun, pilihan kernel dalam SVR dapat mempengaruhi performa prediksi. Penelitian ini bertujuan untuk membandingkan performa prediksi produksi kelapa sawit menggunakan tiga kernel yang berbeda, yaitu linear, polinomial, dan radial basis function (RBF), di PTPN V.Data produksi kelapa sawit dari PT Perkebunan Nusantara V (PTPN V) digunakan sebagai data input. Metrik evaluasi performa prediksi, seperti mean absolute error (MAE), mean squared error (MSE), dan koefisien determinasi (R-squared), digunakan untuk membandingkan ketiga kernel SVR. Hasil eksperimen menunjukkan bahwa kernel RBF cenderung memberikan hasil prediksi yang lebih baik dibandingkan dengan kernel linear dan polinomial. Namun, faktor-faktor seperti kestabilan model dan kecepatan komputasi juga perlu dipertimbangkan dalam pemilihan kernel. Penelitian ini memberikan wawasan penting bagi pengguna SVR dalam memilih kernel yang sesuai untuk meningkatkan prediksi produksi kelapa sawit di PTPN V.
Analysis of User Adaptation to the My Capella Application based on the Coping Model of User Adaptation (CMUA) Mutia, Risma; Megawati, Megawati; Afdal, M.; Permana, Inggih
Sistemasi: Jurnal Sistem Informasi Vol 14, No 4 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i4.5328

Abstract

The My Capella application developed by PT Capella Dinamik Nusantara was designed to facilitate customer access to digital services, particularly for booking Honda motorcycle servicing. However, its use still encounters several challenges, especially regarding user adaptation. These include difficulties in understanding and utilizing features, a complex interface, and insufficient user guidance. This study aims to analyze and identify user adaptation behavior toward the My Capella application in the Pekanbaru area using the Coping Model of User Adaptation (CMUA), which evaluates how users respond to new technologies through cognitive and emotional processes. The research findings support four accepted hypotheses: opportunity appraisal significantly influences problem-focused adaptation; secondary appraisal significantly influences both problem-focused and emotion-focused adaptation; and threat appraisal significantly influences problem-focused adaptation. The strongest effect was observed in the relationship between secondary appraisal and problem-focused adaptation, with a t-statistic of 7.259 > 1.960. These findings indicate that users respond to the My Capella application both cognitively and emotionally, aligning with the CMUA framework and reflecting adaptation processes that are both problem-focused and emotion-focused. Therefore, it is recommended that application developers provide interactive training modules, regular outreach or user engagement sessions, and improvements to the user interface (UI/UX) design to make it more intuitive. These efforts can enhance users' understanding and comfort in using application features—especially during system updates.
Applying KNN, NBC, and C4.5 Algorithms to Identify Eligibility for Non-Cash Food Aid Rizki Pratama Putra Agri; Permana, Inggih Permana; Salisah, Febi Nur; Jazman , Muhammad; Afdal, Muhammad
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/89xvxf70

Abstract

The Indonesian government has implemented the Non-Cash Food Assistance (BPNT) program as an effort to improve people's welfare. However, in its implementation, there are still obstacles in the process of determining the right beneficiaries. Determining the right BPNT recipients is important to ensure that the assistance is received by people who really need it and to prevent budget misuse. This research aims to help the government to easily process data using three classification algorithms, namely K-Nearest Neighbour (K-NN), Naïve Bayes Classifier (NBC), and C4.5 in classifying BPNT recipient data in Air Molek Village, Indragiri Hulu Regency. K-NN, NBC, and C4.5 were chosen because they represent different approaches: K-NN is distance-based, NBC is probability-based, and C4.5 uses decision trees. The stages of the methodology used include data collection, data preprocessing, data splitting (Hold-Out), data balancing and model testing. The results showed that the K-NN algorithm got an accuracy of 70.45%, precision 68.34% recall 72.42%, NBC got an accuracy of 60.58%, precision 58.21%, recall 85.42%, and C 4.5 with an accuracy of 62.56%, precision 59.17%, recall 63.33%. The results of this study can help the government in developing a more objective and data-based decision support system for determining BPNT recipients. The limitation of this research is the use of data that is limited to only one of the data sources.
Analisis Kepuasan Mahasiswa Pekanbaru Pada Aplikasi Flip dengan Metode End User Computing Satisfaction (EUCS) Anggi Widya Atma Nugraha; Inggih Permana; Febi Nur Salisah; Tengku Khairil Ahsyar; M. Afdal
Journal of Informatics, Electrical and Electronics Engineering Vol. 4 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jieee.v4i4.2439

Abstract

A Flip is a Financial Technology (fintech) company providing admin fee-free money transfer services that has been used by more than 10 million users. Along with technological developments in the financial sector, Flip must be able to compete and survive against similar service providers. Efforts that can be made to compete include measuring satisfaction levels in using Flip. The purpose of this study is to assess the level of satisfaction of Flip users so that the results of this research can be used to provide recommendations for evaluating the Flip information system. In conducting satisfaction level analysis, the End User Computing Satisfaction (EUCS) approach can be applied. EUCS is able to evaluate usage satisfaction in using information systems in the areas of content, accuracy, format, ease of use, and timeliness based on information system usage experience. The research was conducted with sample data from university student users of the Flip application in Pekanbaru City. Based on the test results, the highest result with a percentage value of 80% in the Very Satisfied category was observed in the Ease of Use variable from the Likert scale results. The average satisfaction level of Flip application users was 77% in the Satisfied category. The Classical Assumption Test results showed that in the normality test, the testing was normal, and in the multicollinearity testing, it was found that multicollinearity did not occur in the test results. In the Multiple Linear Regression Test, the variable equation result obtained was Y = 0.158 + 0.114X1 + 0.031X2 + 0.054X3 + 0.111X4 + 0.001X5. Based on the Coefficient of Determination Test results, it was found that the content variable, accuracy variable, format variable, ease of use variable, and timeliness variable were able to explain their relationship to the dependent variable and showed an influence of 53%.
Pengukuran Retensi Pelanggan Insyira Oleh-Oleh Berdasarkan Analisis Sentimen Pengguna Instagram Fiki; Inggih Permana; Febi Nur Salisah; Eki Saputra; Arif Marsal
Journal of Informatics, Electrical and Electronics Engineering Vol. 4 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/jieee.v4i4.2473

Abstract

Instagram as a social media platform has opened new opportunities for businesses to market their products creatively and efficiently. Through interactive features such as the comments section, users can express their opinions about the products or services offered. These comments contain sentiments that can be analyzed to understand customer perceptions. This study aims to measure customer retention using sentiment analysis of Instagram user comments. The comment data was collected using web scraping techniques from the Instagram page, followed by labeling using a lexicon-based approach and sentiment classification into positive, negative, and neutral categories through sentiment analysis. This analysis is linked to the concept of customer retention, which is an important strategy for maintaining long-term relationships with consumers. Furthermore, the results of customer retention analysis in this study show that positive sentiment has a retention rate of 53.4% (303 out of 567 comments), neutral sentiment 6.9% (45 out of 650 comments), and negative sentiment 15.1% (22 out of 146 comments). Overall, 370 out of 1,363 comments, or 27.1%, were categorized as contributing to retention. In terms of the proportion of sentiment contributing to total retention, positive comments dominate with 81.9% (303 out of 370). These findings suggest that although neutral comments are the most frequent, positive sentiment contributes the most to customer retention. This indicates that positive sentiment is a strong predictor of customer loyalty, highlighting the importance for companies to foster positive experiences through quality products, reliable services, and active engagement on social media. Insyira is capable of maintaining customer retention, especially from those who express positive sentiment, which reflects satisfaction with its products, services, and interactions on social media
Analisis Sentimen Layanan J&T Express pada Sosial Media X Menggunakan Algoritma Naïve Bayes Clasifier dan K-Nearest Neighbor Priady, Muhamad Ilham; Afdal, M.; Permana, Inggih; Zarnelly, Zarnelly
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.v6i4.7721

Abstract

The demand for goods delivery services is increasing along with the widespread use of e-commerce platforms for buying and selling. One of the popular and frequently used delivery service providers is J&T Express. Until now, J&T has had a wide service coverage. However, various customers also have complaints that are often conveyed through social media X. For this reason, this study conducted a sentiment analysis of J&T Express user opinions on social media X using the Naïve Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) algorithms. Data collection was carried out through scraping over a time span from January 1, 2023 to December 1, 2024, resulting in a total of 1,000 data points. The modeling results show that the NBC algorithm outperforms KNN, achieving an accuracy of 72.30%, a precision of 74.76%, and a recall of 72.30%. Meanwhile, the KNN algorithm with the best parameters (K = 9) only has an accuracy of 67.29%, precision of 69.46%, and recall of 67.29%. Then the results of the analysis show that J&T user opinions are dominated by negative sentiment (42.20%), followed by positive sentiment (38.70%) and neutral sentiment (19.10%). Further analysis based on five variables was also conducted and an understanding of J&T's weaknesses, namely in the service aspect, with the highest negative sentiment (21.0%). On the other hand, the user experience aspect is an advantage with the most positive sentiment (16.8%). The data visualization results also indicate that there are dominant customer complaints about the delay in the delivery process. However, customers also appreciate the speed and security of the delivery of goods. These findings provide valuable insights for J&T Express to conduct evaluations and improvements, especially in the service aspect, to improve overall customer satisfaction and experience.
Evaluating the Impact of Data Balancing Techniques on the k-Nearest Neighbors Algorithm for Microarray Data Classification Febi Nur Salisah; Inggih Permana; Sanusi; Shir Li Wang
Jurnal Inotera Vol. 10 No. 2 (2025): July - December 2025
Publisher : LPPM Politeknik Aceh Selatan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31572/inotera.Vol10.Iss2.2025.ID497

Abstract

Microarray data classification poses significant challenges in bioinformatics due to the nature of the data, which has a very high number of features but a limited number of samples, and an unbalanced class distribution. This condition can cause a decrease in the performance of classification models, including k-Nearest Neighbor (kNN). This study aims to evaluate the performance of the kNN algorithm in classifying unbalanced and balanced data. The balancing techniques used are Random Undersampling (RUS), Random Oversampling (ROS), and Synthetic Minority Over-sampling Technique (SMOTE). The datasets used in this study are three leukemia datasets with different class structures, namely two, three, and four classes. The experimental results show that the ROS and SMOTE techniques consistently improve the performance of kNN, with the best accuracy reaching more than 97%. In the two-class dataset, ROS gave the best performance (99.4%), while in the three-class dataset, SMOTE showed the most optimal results (98.5%). In the four-class dataset, the performance improvement due to balancing was very significant; SMOTE and ROS were able to improve the accuracy from 89.7% (without balancing) to 99.0% and 98.8%, respectively. Although RUS recorded perfect accuracy of 100%, the results were anomalous and inconsistent. RUS showed less stable performance and was often lower than the condition without balancing, especially on datasets with four classes. Overall, the SMOTE technique proved to be the most stable and effective for various class structures. This study shows the importance of balancing strategies in the classification of complex and imbalanced microarray data.
Analisa Sentimen Pengguna Aplikasi DANA Pada Ulasan Google Play Store Menggunakan Algoritma Naive Bayes Classifier dan K-Nearest Neighbors Sabillah, Dian Ayu; Afdal, M; Permana, Inggih; Muttakin, Fitriani
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7861

Abstract

The use of digital wallets such as DANA in Indonesia continues to increase along with the need for fast and practical non-cash transactions. User reviews on the Google Play Store are an important source of information to assess satisfaction and service problems. This study aims to classify user sentiment towards the DANA application using the Naïve Bayes Classifier (NBC) and K-Nearest Neighbor (KNN) algorithms. A total of 1,000 reviews were collected and processed through text cleaning, tokenization, stopword removal, and stemming. Sentiments were classified into positive, neutral, and negative using the lexicon method and expert validation. The results showed that NBC was superior to KNN, with the highest accuracy of 71.83%, while KNN only reached 56.44%. NBC was also more effective in detecting negative sentiment, although both were less than optimal for neutral sentiment. Word cloud visualization displays the dominant words in each sentiment category. The conclusion of this study states that Naïve Bayes is more effective in analyzing sentiment reviews of digital wallet applications such as DANA.
Co-Authors Aditya Nugraha Yesa Agus Buono Ahsyar, Tengku Khairil Al Kiramy, Razanul Alfakhri, Rezky Alfaridzi, Gemma Tahmid Aliya, Rahma Andi Darlianto Andriyani, Dwi Ratna Anggi Widya Atma Nugraha Anggia Anfina Anisah Fitri Anjani, Yulia Merry Annisa Ramadhani Aprijon Arif Marsal Arif Marsal Arif Marsal Arifah Fadhila Andaranti Arifin, Abdullah Aufa Zahrani Putri Aulia Dina Bib Paruhum Silalahi Chinthia, Maulidania Mediawati Dedi Pramana Dessi Cahyanti Detha Yurisna Detha Yurisna Devi, Rahma Dzul Asfi Warraihan Eka Pandu Cynthia Eki Saputra Eki Saputra Endah Purnamasari Esis Srikanti Fadhilah Syafria Fadil Rahmat Andini Farahdina Risky Ramadani Febi Nur Salisah Febi Nur Salisah Fiki Fikri, M. Hayatul Fitriah, Ma’idatul Fitriah, Ma’idatul Fitriani Muttakin Fitriani Muttakin Fitriani Muttakin Gathot Hanyokro Kusuma Gurning, Umairah Rizkya Hafiz Aryan Siregar Hasbi Sidiq Arfajsyah Hendri, Desvita Hilda Mutiara Nasution Husaini, Fahri Idria Maita Idria Idriani R, Nova Ikhsani, Yulia Imam Muttaqin Intan, Sofia Fulvi Ismail Marzuki Jazma, Muhammad Jazman , Muhammad Jazman, Muhammad Kusuma, Gathot Hanyokro M Afdal M Afdal M Zaky Ramadhan Z M. Afdal M. Afdal M. Afdal M. Afdal M. Afdal Maulana, Rizki Azli Megawati Megawati - Mona Fronita, Mona Muhammad Afdal Muhammad Fikry Muhammad Jazman Muhammad Jazman Muhammad Jazman Muhammad Naufal, Muhammad Muhammad Zacky Raditya Mukmin Siregar Mundzir, Mediantiwi Rahmawita Munzir, Medyantiwi Rahmawita Mustakim Mustakim Mustakim Mustakim Mustakim Mustakim Mutia, Risma Muttakin, Fitriani Nabillah, Putri Nardialis Nardialis Nasution, Nur Shabrina Naufal Fikri, R. Adlian Negara, Benny Sukma Nesdi Evrilyan Rozanda Nesdi Evrilyan Rozanda Nisa', Sayyidatun Norhavina Norhavina Nunik Noviana Kurniawati Nurainun Nurainun Nuraisyah Nuraisyah Nurfadilla, Nadia Nurkholis Nurkholis nursalisah, febi Octavia, Sania Fitri Pratama, Arya Yendri Priady, Muhamad Ilham Pristiawati, Andani Putri Puput Iswandi Putra, Moh Azlan Shah Putra, Tandra Adiyatma Rahman, Eman Rahmawita M, Medyantiwi Rangga Arief Putra Rayean, Rival Valentino Restu Ramadhan Ria Agustina Rice Novita Rice Novita Rizka Fitri Yansi Rizki Pratama Putra Agri Rozanda, Nesdi Evrilyan Sabillah, Dian Ayu Salisah, Pebi Nur Sania Fitri Octavia Sanusi Shir Li Wang Siti Monalisa Sofia Fulvi Intan Susanti, Pingki Muliya Tasya Marzuqah Tengku Khairil Ahsyar Triningsih, Elsa Tshamaroh, Muthia Uci Indah Sari Ula, Walid Alma Vicky Salsadilla Wenda, Alex Wido Purnama Winda Wahyuti Windy Amelia Putri Wira Mulia, M. Roid Yusmar Yusmar Zarnelly Zarnelly Zarnelly Zarnelly Zarnelly Zarnelly Zarnelly