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All Journal Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Building of Informatics, Technology and Science Dinasti International Journal of Education Management and Social Science Jurnal Teknologi Dan Sistem Informasi Bisnis Abdimas: Jurnal Pengabdian Masyarakat Universitas Merdeka Malang Journal of Computer System and Informatics (JoSYC) Jurnal Teknik Informatika (JUTIF) Instal : Jurnal Komputer International Journal Of Computer, Network Security and Information System (IJCONSIST) Jurnal Sistem Informasi dan Bisnis Cerdas Ekasakti Pareso Jurnal Akuntansi SmartComp Intechno Journal : Information Technology Journal
Pratama, Irfan
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Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Social Sciences

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SISTEM PENDUKUNG KEPUTUSAN PENERIMAAN BLT MENGGUNAKAN METODE WEIGHTED AGGREGATED SUM PRODUCT ASSESMENT (WASPAS): DECISION SUPPORT SYSTEM RECEIVING BLT USE METHOD WEIGHTED AGGREGATED SUM PRODUCT ASSESMENT (WASPAS) Priatama, Candra; Pratama, Irfan
Jurnal Sistem Informasi dan Bisnis Cerdas Vol. 15 No. 2 (2022): Agustus 2022
Publisher : Program Studi Sistem Informasi, Fakultas Ilmu Komputer, UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (358.896 KB) | DOI: 10.33005/sibc.v15i2.19

Abstract

Bantuan Langsung Tunai (BLT) merupakan salah satu program bantuan sosial kepada warga kurang mampu yang berdomisili di daerah setempat. Pemberian keputusan kepada penerima BLT harus dilakukan dengan baik dan tepat sasaran. Selama ini pemberian dana bantuan sosial di Desa Cintajaya masih menggunakan masih menggunakan aplikasi pendukung seperti Microsoft Excel dan belum adanya database yang mendukung sehingga keakuratan data kurang terjamin. Dari akar permasalahan tersebut dibangunlah sebuah Sistem Pendukung Keputusan untuk menentukan penerima BLT di Desa Cintajaya dengan menggunakan metode Weighted Aggregated Sum Product Assesment (WASPAS). Dalam penelitian ini diperlukan kriteria dan alternatif untuk mendapatkan solusi pengambilan keputusan penerimaan BLT. Sistem pendukung keputusan tersebut berbasis web agar penggunaanya efektif dan efisien. Berdasarkan hasil pengujian dapat disimpulkan dari 83 alternatif di peroleh bahwa kandidat (ranking teratas) yang layak menerima Bantuan Langsung Tunai adalah 35 alternatif, dengan batasan nilai preferensi (Qi) yang berhak menerima bantuan minimal 0.46074091161547.
Analisis Sentimen Ulasan Aplikasi Identitas Kependudukan Digital Menggunakan Metode Support Vector Machine Guno Wibowo, M Ioni Abdurrahman; Pratama, Irfan
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 6 No 4 (2024): Oktober 2024
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v6i4.1552

Abstract

Digital Population Identity Application, developed by the Directorate General of Civil Registration of the Ministry of Home Affairs, Indonesia, represents a digitalization initiative for population documents including electronic ID cards (KTP-el), Family Cards, Covid-19 vaccination certificates, tax identification numbers (NPWP), vehicle ownership information, National Civil Service Agency (BKN) data, social security (BPJS), national socioeconomic data (DTKS), and voter lists. Sentiment analysis is crucial to understand user feedback on the application. This study aims to analyze user sentiment toward the Digital Population Identity Application on Google Play Store, categorize sentiments using ISO 9126 standards, and evaluate accuracy using Support Vector Machine (SVM) algorithms within the framework of the Cross-Industry Standard Process for Data Mining (CRISP-DM). Research findings indicate positive sentiment from users toward the Digital Population Identity Application, with a primary focus on application functionality in positive reviews. SVM models trained using lexicon-based labeling achieved an accuracy of 80%, while models trained with ISO 9126 labeling achieved 84% accuracy. The conclusion of this study is that the Digital Population Identity Application is well-received by users, providing valuable guidance for developers to improve the quality and future development of the application.
Penerapan Metode Content Based Filtering pada Sistem Rekomendasi Pemilihan Produk Skincare Iqbal, Sayid Muhammad; Pratama, Irfan
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 13, No 3 (2024): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v13i3.7156

Abstract

Abstrak: Kulit memiliki peran penting sebagai organ terluar yang melindungi dan menyelimuti tubuh manusia. Kurangnya memahami faktor penyebab masalah kulit serta tidak mengetahui jenis kulit sehingga menggunakan skincare tanpa pertimbangan matang sering kali terjadi. Penerapan teknologi dalam sistem rekomendasi produk skincare dengan banyaknya pilihan metode yang dapat diaplikasikan semakin berkembang. Sehingga dapat membantu pelanggan untuk mendapatkan rekomendasi sesuai dengan kriteria masalah pada setiap jenis kulit. Content-Based Filtering adalah salah satu metode yang dapat merekomendasikan atas dasar kemiripan atribut dari produk yang telah dinilai oleh penggunanya. Nilai dari perhitungan yang didapatkan menunjukkan bahwa pasangan dokumen dengan nilai cosine similarity tertinggi adalah antara Q dan D5 dengan nilai 0.411, diikuti oleh Q dan D2 dengan nilai 0.332, Q dan D3 dengan nilai 0.292, Q dan D4 dengan nilai 0.260, dan terakhir Q dan D1 dengan nilai 0.195. Peringkat ini menunjukkan bahwa dokumen D5 paling mirip dengan query Q, sedangkan dokumen D1 memiliki kemiripan paling rendah Hasil penelitian dapat menunjukan nilai cosine similarity dari setiap produk yang disarankan.  Kata Kunci – Sistem Rekomendasi; TF-IDF; Cosine Similarity; Skincare; 
Classification of Air Pollutant Index on Data with Outliers and Imbalance Class Problem Krisbiantoro, Dwi; Waluyo, Retno; Hasanah, Uswatun; Pratama, Irfan; Sarmini, Sarmini
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.1993

Abstract

The problem of air pollution has become a global issue that has received attention from various countries. Jakarta, Indonesia's capital city, is unavoidable from the same problem. This study will use four parameters of substances PM10, SO2, CO, O3, and nitrogen dioxide to categorize Jakarta's air quality (NO2). The data used is daily data taken from the Air Quality Monitoring Station in Jakarta throughout 2020. The methods used include SVM, Random Forest, Logistic Regression, KNN, CART, and Stacking Algorithm. At the data preparation stage, we found missing values, outliers, and class imbalance problems. Before applying machine learning methods and evaluating accuracy, we used data pre-processing techniques such as the MissForest method, median substitution, and ADASYN. The results prove that the original dataset has a higher accuracy score (0.882 – 0.977) than the balanced dataset (0.829 – 0.976). According to the evaluation results, the Random Forest method has the highest accuracy score for original and balanced datasets. The overall result is better than the identical research, which produces 96.61% accuracy using a neural network. It shows that preprocessing steps such as missing values handling an imbalanced class handling is essential in classification performance.
PREDIKSI JUMLAH KEDATANGAN WISATAWAN MANCANEGARA DI INDONESIA BERDASARKAN PINTU MASUK KEDATANGAN UDARA Prayuda, Arya; Pratama, Irfan
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 9 No 2 (2024): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v9i2.4787

Abstract

Indonesia has diversity and natural wealth that attracts tourism in Indonesia. Tourism is one of the industries that provides the highest foreign exchange for the country because it has a positive impact. However, the existence of COVID-19 has resulted in a decrease in the number of visits due to restrictions on foreign tourists. From January to November 2021, there was a drastic decrease of 61.82% in the number of foreign tourist visits compared to the same period in 2020. In addition to COVID-19, as well as support in building facilities that support the increase in the number of foreign tourists. From these conditions, predictions are needed that are used as a basis for planning and helping decision making. The purpose of this study is to develop a more accurate prediction model in similar studies using the same data in predicting foreign tourist arrivals in Indonesia through air entrances using the XGBoost, Random Forest, and Catboost methods by focusing on the accuracy evaluation results metrics RMSE, MAE, and MAPE and making predictions for the next 12 months. The dataset used is taken from the Central Statistics Agency (BPS), namely data on foreign tourist arrivals based on the arrival entrance in the period January 2017 to November 2021. The data used are time series and non-stationary. From the research results, it can be seen based on the accuracy evaluation results that the XGBoost model of this study gets better accuracy evaluation results than the other two models by getting the results of the RMSE accuracy evaluation value of 671935.2, MAE 648139.1, and MAPE 20985.35. The XGBoost model is better with a smaller accuracy error value than the Random Forest model, Catboost, and similar research using the ARIMA method with an RMSE value of 779670.7, MAE 749030.4, and MAPE 23196.45.
PENANGANAN MISSING VALUES DAN PREDIKSI DATA TIMBUNAN SAMPAH BERBASIS MACHINE LEARNING Widianti, Anisa; Pratama, Irfan
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 9 No 2 (2024): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v9i2.4789

Abstract

The issue of increasing waste due to the growing population and human activities presents a serious challenge in waste management in Central Java. One of the main obstacles in waste prediction research is the prevalence of missing data, which can reduce the accuracy of predictive models. This study employs three methods to handle missing values: Mean Imputation, Interpolation, and KNN Imputer. Once the missing values are filled using these methods, the next step is to calculate the prediction values. The study utilizes three predictive models: Random Forest, Gradient Boosting, and KNN. The results indicate that with Mean Imputation, the Random Forest model shows the best performance with an RMSE of 0.349. When using Interpolation for missing values, the Gradient Boosting model becomes the best choice with an RMSE of 0.543. Meanwhile, with KNN Imputer, the Gradient Boosting model again performs the best with an RMSE of 0.188. Based on this research, the most effective approach is using KNN Imputer for handling missing values in conjunction with the Gradient Boosting model. This combination provides the lowest RMSE for similar datasets.
Implementasi Voice To Text Pada Invoice Checking Berbasis Web Swapurba, Gangsar; Pratama, Irfan
Intechno Journal : Information Technology Journal Vol. 5 No. 2 (2023): December
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/intechnojournal.2023v5i2.1391

Abstract

INDOMASCOT adalah perusahaan yang bergerak di bidang konveksi kostum badut maskot. Tim sales-nya seringkali mengalami kerepotan untuk mengirim invoice kepada customer khususnya ketika berada di workshop. Maka dari itu, perlu adanya satu solusi dalam bentuk aplikasi invoice checking yang dapat mengurangi beban pekerjaan tim sales dalam pengiriman invoice kepada customer. Selain itu, sebagai fitur tambahannya baik sekali untuk disematkan pengimplementasian AI dalam bentuk voice-to-text agar customerdapat menginput email & no. invoice-nya dari sumber suara. Dari beberapa kali percobaan, Web Speech APIini kurang memuaskan untuk mengenali alamat email. Namun, memiliki hasil memuaskan untuk mengenali no. invoice yang berbentuk angka. Fitur voice-to-text ini memanfaatkan Web Speech API yang saat tulisan ini dibuat masih dalam status experimental.
Klastering Kecepatan Internet Operator Telkomsel Berdasarkan Sebaran Site BTS (Base Transceiver Station) Menggunakan Metode DBSCAN Alifudin, Arif; Pratama, Irfan
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.5984

Abstract

The development of cellular telecommunications technology has now reached a more advanced stage with the presence of 4G LTE technology. Compared to previous technologies such as 3G and 2G, this technology offers data transfer capabilities with much better access speeds. This makes 4G LTE the backbone for modern communication services that support users' needs for fast and stable internet access. However, even though 4G LTE technology has been widely implemented, there are still challenges that need to be overcome to ensure network quality remains optimal. The quality of the internet network in the Special Region of Yogyakarta (DIY) has experienced significant improvements in recent years, but obstacles such as limited infrastructure are still felt, especially in rural and outermost areas. This research aims to analyze and group areas based on network quality. Therefore, data mining analysis of existing data is needed using the DBSCAN algorithm so that clusters will be formed which are divided according to network quality. After carrying out analysis using the epsilon value = 0.5 and the minpts value = 5, the clusters formed were 5 clusters with a silhouette value of 0.216471397367446, which indicates that the quality of the clustering is relatively low, which is possibly caused by less than optimal distribution of data or parameters. Nevertheless, the clustering results obtained still provide useful insights for analyzing site distribution and network performance.
Analisis Sentimen Terhadap Ulasan Hotel Melalui Platform Google Review Menggunakan Metode Stacking Wibowo, Edi; Pratama, Irfan
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 6 No 4 (2024): Oktober 2024
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v6i4.1475

Abstract

In the digital era, the internet has become the main source of information for the public, including when searching for hotel information. One platform frequently used for finding hotel information is Google Review, which allows hotel guests to share their experiences. This study aims to analyze sentiments towards hotel reviews, specifically for hotels in Jakarta, through Google Review to help customers choose hotels that meet their needs. The research method uses Serp API in Python to gather review data from Google Review, followed by data preprocessing and labeling with VADER. Sentiment classification is conducted using a stacking ensemble method. The stacking ensemble method in this study employs Naive Bayes, Random Forest, Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Extra Trees Classifier as base algorithms. The sentiment classification results are then evaluated using precision, F1-score and accuracy metrics. The research findings show that the stacking ensemble method with Naive Bayes, Random Forest, Logistic Regression, Support Vector Machine, K-Nearest Neighbor, and Extra Trees Classifier algorithms performs better than individual models. The precision, F1-Score and accuracy of the stacking ensemble method reach 91%, 97%, 98%, respectively.
Pengaruh Kepemilikan Institusional, Dewan Komisaris Independen, Kepemilikan Manajerial dan Intensitas Persedian Terhadap Agresivitas Pajak (Studi Empiris pada Perusahaan Makanan dan Minuman yang terdaftar di Bursa Efek Indonesia Periode Tahun 2016-2019) Pratama, Irfan; Rina Asmeri; Andre Bustari
Ekasakti Pareso Jurnal Akuntansi Vol. 1 No. 3 (2023): Ekasakti Pareso Jurnal Akuntansi (Juli 2023)
Publisher : Fakultas Ekonomi, Universitas Ekasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31933/epja.v1i3.916

Abstract

This study aims to determine the effect of Institutional Ownership, Independent Board of Commissioners, Managerial Ownership and Inventory Intensity on Tax Aggressiveness in Food and Beverage Companies listed on the Indonesia Stock Exchange 2016-2019. The type of statistics used in this have a look at is quantitative records taken from the Indonesia inventory exchange. The population in this study are Food and Beverage Companies listed on the Indonesia Stock Exchange in 2016-2019, as many as 38 companies. The sample in this study was 10 samples taken through purposive sampling method. The analytical method used in this research is multiple linear regression analysis with classical assumption test using SPSS 25.0 application. The results of this study indicate that the influence of institutional ownership and managerial ownership partially has no significant effect on tax aggressiveness in food and beverage companies. The influence of the Independent Board of Commissioners partially has a positive effect and the Influence of Inventory Intensity partially has a significant negative effect on Tax Aggressiveness in Food and Beverage Companies. Simultaneously the influence of institutional ownership, independent board of commissioners, managerial ownership and inventory intensity have a significant effect on aggressive decisions.
Co-Authors Ade Kurnia, Dian Akbar, Muhammad Rizky Mawandhyka Albert Yakobus Chandra Alifudin, Arif Andre Bustari Asanah, Nurul Ayuningsih, Ekatri Dadang Sudrajat Dwi Krisbiantoro, Dwi Edi Wibowo Guno Wibowo, M Ioni Abdurrahman Hubu, Muhammad Rizki Iqbal, Sayid Muhammad Kurniawan, Akbar Sidiq Kurniawan, Muhammad Rifki Manurung, Syalom Kristian Odi Nurdiawan Prasetyaningrum, Putri Taqwa Prayuda, Arya Priatama, Candra Rina Asmeri, Rina Sarmini Sarmini Suria, Ozzi Swapurba, Gangsar Taqwa Prasetyaningrun, Putri Uswatun Hasanah Waluyo, Retno Widianti, Anisa