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Jurusan Sistem Informasi Universitas Islam Negeri Sultan Syarif Kasim Riau
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Peramalan Jumlah Kedatangan Wisatawan Menggunakan Support Vector Regression Berbasis Sliding Window Fitriah, Ma’idatul; Permana, Inggih; Salisah, Febi Nur; Munzir, Medyantiwi Rahmawita; Megawati, Megawati
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7408

Abstract

As a developing city, Pekanbaru has the potential for attractive tourist attractions for tourists. The arrival of tourists has had a big positive impact on the economy of Pekanbaru City. The number of tourist arrivals can experience ups and downs every month, for this reason it is necessary to forecast the number of tourists in the future. This research aims to apply the Orange Data Mining application in predicting the number of tourist arrivals by comparing the kernels in the Support Vector Regression (SVR) method and applying Sliding Window size 3 to window size 13 to transform into time series data. As well as sharing data using the K-Fold Validation method with a value of K-10. Then the performance of the kernels used can be seen using the Test and Score widget which presents the results of Root Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), dan R-squared(R2). The results for forecasting the number of tourist arrivals to Pekanbaru City using the SVR method show that the RBF Kernel is the optimal choice compared to the Polinomial and Linear Kernels. The results of the Test and Score widget show that the RBF Kernel with window size 10 has lower MAE, MSE and RMSE values, namely 0.118, 0.022 and 0.147. Apart from that, the comparison of R2 in window size 10 for Kernel RBF shows better performance with a value of 0.519.
Klasifikasi Penerima Bantuan Program Indonesia Pintar (PIP) Pada Siswa SMK Menggunakan Algoritma KNN, NBC dan C4.5 Putra, Tandra Adiyatma; Permana, Inggih; Zarnelly, Zarnelly; Megawati, Megawati
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.v6i4.6395

Abstract

The Indonesia Smart Program (PIP) is a government initiative aimed at providing educational assistance to students from underprivileged families. This research was conducted at SMKN 4 Pekanbaru to enhance the accuracy of distributing PIP aid using data mining methods. Three classification algorithms were used to identify students eligible for assistance: K-Nearest Neighbor (KNN), Naive Bayes Classifier (NBC), and C4.5. The data used in this study included attributes such as parental occupation, income, and the type of transportation used. The data processing involved cleaning, normalization, and splitting into test and training sets. The results showed that the KNN algorithm performed best with an accuracy of 84.20%, precision of 89.83%, and recall of 99.18%. The C4.5 algorithm excelled in model simplicity, while NBC showed less optimal results compared to KNN.
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 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.
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): Juli 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.
OPTIMALISASI STRATEGI PROMOSI BERDASARKAN WAKTU DAN JENIS PRODUK MENGGUNAKAN ALGORITMA FP-GROWTH Andaranti, Arifah Fadhila; Afdal, M.; Permana, Inggih; Jazman, Muhammad; Marsal, Arif
Jurnal Sistem Informasi dan Informatika (Simika) Vol. 8 No. 2 (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/dy69fk12

Abstract

Aba Mart is a convenience store that provides a wide range of daily necessities. One of the challenges faced by Aba Mart is the uncertainty in determining the optimal timing for product promotions. To address this issue, this study utilizes sales transaction data obtained from the store’s Point of Sale (POS) system, totaling 12,887 transactions recorded from March to August 2024. The dataset includes attributes such as date and product name, which were processed through attribute selection, categorization into 33 product types, conversion of dates to days, and transformation into boolean format for analysis. The study applies the Association Rule Mining (ARM) technique using the Frequent Pattern Growth (FP-Growth) algorithm to identify the relationship between the time of purchase and the types of products bought. The results demonstrate that the FP-Growth algorithm successfully identified patterns of association. By testing with minimum support values of 2%, 3%, and 4%, and a minimum confidence of 10%, the analysis produced 15 association rules in March, 11 in April, 14 in May, 13 in June, 11 in July, and 13 in August 2024. These rules have been used as a foundation for formulating more effective and targeted promotional strategies for Aba Mart.
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.
Evaluasi Usability Aplikasi Mobile Banking Menggunakan Metode Retrospective Think Aloud dan Post-Study System Usability Questionnaire Naufal, Muhammad; Ahsyar, Tengku Khairil; Jazman, Muhammad; Permana, Inggih
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.v13i3.4039

Abstract

BRKS Mobile is a digital service provided by Bank Riau Kepri Syariah to facilitate its customers in conducting financial transactions via smartphones. Because this application is relatively new, there are problems when running the application. The results of user reviews on playstore comments and pre-surveys, the problem that often occurs is errors when making transactions. In this study, usability evaluation was carried out using the Retrospective Think Aloud (RTA) and Post-Study System Usability Quesionaire (PSSUQ) methods. The results of the usability measurement show that users experience little difficulty when running the transfer and purchase menus. This is reinforced by the results contained in the norms of the PSSUQ method where the results of the SyeUse variable value of 2.70 and InfoQual 2.95 are below the average which indicates that the usability of the system and the quality of information on BRKS Mobile are still lacking. For the InterQual value of 3.09, it is above average and overall the BRKS application is at 2.89 above average, which means that the application can be accepted by its users.
Co-Authors Ahsyar, Tengku Khairil Al Kiramy, Razanul Alfakhri, Rezky Andaranti, Arifah Fadhila Annisa Ramadhani Arif Marsal Arifin, Abdullah Febi Nur Salisah, Febi Nur Febi Nur Salisah, Febi Nur Salisah Fikri, M. Hayatul Fitriah, Ma’idatul Fitriah, Ma’idatul Idriani R, Nova Ikhsani, Yulia Intan, Sofia Fulvi Jazman, Muhammad Kusuma, Gathot Hanyokro M Afdal M. Afdal M. Afdal Maulana, Rizki Azli Megawati Megawati - Mona Fronita, Mona Muhammad Naufal, Muhammad Mundzir, Mediantiwi Rahmawita Munzir, Medyantiwi Rahmawita Mustakim Mustakim Mutia, Risma Muttakin, Fitriani Nabillah, Putri Nisa', Sayyidatun Nurkholis Nurkholis Priady, Muhamad Ilham Pristiawati, Andani Putri Putra, Moh Azlan Shah Putra, Tandra Adiyatma Rahmawita M, Medyantiwi Rice Novita Rozanda, Nesdi Evrilyan Sabillah, Dian Ayu Salisah, Febi Nur Salisah Salisah, Pebi Nur Susanti, Pingki Muliya Triningsih, Elsa Ula, Walid Alma Windy Amelia Putri Zarnelly Zarnelly Zarnelly