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All Journal Elkawnie JOIN (Jurnal Online Informatika) Jurnal Teknik Informatika (JUTIF) EXPLORER Intellect : Indonesian Journal of Learning and Technological Innovation ISTEK
Agung Wahana
Fakultas Teknologi dan Sain, Universitas Islam Negeri Bandung
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Education Electrical & Electronics Engineering Engineering

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Analisis Sentimen Ulasan Aplikasi Bank Digital Menggunakan Algoritma Naïve Bayes Adelia Irawan, Febby; Rialdy Atmadja, Aldy; Wahana, Agung
Explorer Vol 4 No 2 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/explorer.v4i2.1181

Abstract

Bidang perbankan merupakan salah satu yang berkembang dan mengikuti tren digitalisasi. Adanya bank digital merupakan inovasi yang dilakukan pada bidang perbankan dalam memberikan pelayanan dengan menggunakan media elektronik atau digital. Teknologi yang dikembangkan memungkinkan pengguna hanya cukup mengakses transaksi dalam suatu aplikasi dengan bermodalkan smartphone yang didistribusikan melalui Google Playstore. Ulasan-ulasan pengguna (review) pada Google Playstore ini tersedia untuk membantu meningkatkan performa dari aplikasi dan menjadi landasan bagi perusahaan dalam mengembangkan aplikasi perbankan. Akan tetapi, terdapat kendala jika banyaknya ulasan dan sulit untuk memilah dan mengolahnya secara manual sehingga diperlukan analisis sentimen ulasan pengguna pada aplikasi-aplikasi bank digital. Pada penelitian ini analisis sentimen dilakukan dengan menggunakan algoritma Naïve Bayes. Adapun pendekatan metode yang dilakukan dengan menggunakan CRISP-DM sebagai standar yang umum dalam melakukan riset data mining. Hasil dalam penelitian ini menunjukkan bahwa penerapan model klasifikasi dengan menggunakan Algoritma Naïve Bayes dengan data ulasan menghasilkan 46% ulasan positif dan 54% ulasan negatif. Selain itu, nilai akurasi tertinggi dari kinerja algoritma Naïve Bayes dengan menggunakan pembagian data training dan testing dengan persentase 70:30 menghasilkan akurasi yang optimal mencapai 89%.
Implementasi Algoritma K-Nearest Neighbor (KNN) untuk Analisis Sentimen Pengguna Aplikasi Tokopedia Lillah, M. Rival Ridautal Lillah; Maylawati, Dian Sa’adillah; Zulfikar, Wildan Budiawan; Uriawan, Wisnu; Wahana, Agung
Intellect : Indonesian Journal of Learning and Technological Innovation Vol. 2 No. 2 (2023): Intellect : Indonesian Journal of Learning and Technological Innovation
Publisher : Yayasan Lembaga Studi Makwa

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

Abstract

A marketplace is a platform where sellers can come together and sell their goods or services to customers without physical meetings. In the past few decades, marketplaces have become the most popular platform for business sellers to sell their products. Becoming the number 1 marketplace in Indonesia with the most visitors on average is the right marketplace in 2023, namely Tokopedia. However, most people are skeptical of products they have never purchased or used. User reviews play an important role in product marketing, especially on Tokopedia. Reviews help potential customers build trust in the products and services offered by the seller. To analyze reviews quickly and precisely, a sentiment analysis process is needed. Natural Processing Language (NLP) and text mining algorithms are used to classify reviews as positive, or negative. One of the methods used is the K-Nearest Neighbor (KNN) algorithm, which is used to classify Tokopedia user reviews in the Play Store and App Store. The dataset consists of 1000 comment data from the Play Store and 1000 data from the App Store. A total of 2000 comments consisting of 2 labels, namely positive and negative for modeling. Meanwhile, for testing, there were 885,092 comments from the Play Store and 4000 comments from the App Store. Total 889,092 for unlabeled test data. The prediction results on the app store dataset show that there are 97.0% positive label predictions and only 3.0% negative label predictions. Abstrak Marketplace adalah platform tempat penjual dapat berkumpul dan menjual barang atau jasa mereka kepada pelanggan tanpa pertemuan fisik. Dalam beberapa dekade terakhir, pasar telah menjadi platform paling populer bagi penjual bisnis untuk menjual produk mereka. Menjadi marketplace nomor 1 di Indonesia dengan rata-rata pengunjung terbanyak adalah marketplace yang tepat di tahun 2023 yaitu Tokopedia. Namun, kebanyakan orang skeptis terhadap produk yang belum pernah mereka beli atau gunakan. Ulasan pengguna memegang peran penting dalam pemasaran produk, terutama di Tokopedia. Ulasan membantu calon pelanggan membangun kepercayaan terhadap produk dan layanan yang ditawarkan oleh penjual. Untuk menganalisis ulasan dengan cepat dan tepat, diperlukan proses analisis sentimen. Natural Processing Language (NLP) dan algoritma text mining digunakan untuk mengklasifikasikan ulasan sebagai positif, atau negatif. Salah satu metode yang digunakan adalah algoritma K-Nearest Neighbor (KNN), yang digunakan untuk mengklasifikasikan ulasan pengguna Tokopedia di play store dan app store. Dataset terdiri dari 1000 data komentar dari play store dan 1000 data dari app store. Total 2000 komentar yang terdiri dari 2 label yaitu positif dan negatif untuk pemodelan. Sedangkan untuk pengujian 885.092 komentar dari play store dan 4000 komentar dari app store. Total 889.092 untuk data pengujian yang belum dilabeli. Hasil prediksi pada dataset app store menunjukkan terdapat 97,0% prediksi label positif dan hanya 3,0% prediksi label negatif.
IMPLEMENTATION OF CONVOLUTIONAL NEURAL NETWORK USING MOBILENETV2 TO DISTINGUISH HUMAN AND ARTIFICIAL INTELLIGENCE PAINTING Santosa, Dwi Bagia; Wahana, Agung; Uriawan, Wisnu
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 1 (2025): JUTIF Volume 6, Number 1, February 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.1.3827

Abstract

The advancement of artificial intelligence technology has had a significant impact on various fields, including painting. Artificial intelligence is now able to create works of art that resemble paintings produced by humans with a high level of detail and complexity. However, this progress has also created new problems in the world of painting, namely the difficulty in distinguishing between works produced by humans and those created by artificial intelligence. This problem has an impact on the originality of the artwork and has implications for aspects of ethics and creativity. This study aims to develop a deep learning model that can classify human and artificial intelligence paintings, and overcome the challenges in distinguishing between the two. The methodology used is the Cross Industry Standard Process for Data Mining (CRISP-DM), with a dataset consisting of 1,000 painting images. The architecture used is MobileNetV2, implemented using TensorFlow to build a Convolutional Neural Network (CNN). Techniques such as data preparation, data labeling, data splitting, resizing, and data augmentation are applied to improve model performance. Six test scenarios were carried out with variations in the learning rate, number of epochs, and freeze or unfreeze configurations on the base model. The results showed that the best model with a learning rate of 0.0001, base model unfreeze, and 5 epochs managed to achieve an accuracy of 97%, without any indication of overfitting or underfitting. This model was then implemented on an Android application in TFLite format, which can predict image classes with a confidence level of 89.98%.
Implementation of the Simple Multi Attribute Rating Technique Method (SMART) in Determining Toddler Growth Wahana, Agung; Alam, Cecep Nurul; Rohmah, Siti Nur
JOIN (Jurnal Online Informatika) Vol. 5 No 2 (2020)
Publisher : Department of Informatics, UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/join.v5i2.634

Abstract

Toddler nutritional status is an important factor in efforts to reduce child mortality. The development of community nutrition can be monitored through the results of recording and reporting of community nutrition improvement programs reflected in the results of weighing infants and toddlers every month at the Pos Pelayanan Terpadu (Posyandu/ Integrated Service Post) , where these efforts aim to maintain and improve health and prevent and cope with the emergence of public health problems, especially aimed at toddlers. However, in carrying out the health service activities of Medical Officers, faced with an important problem that is still difficult in providing information related to the results of monitoring the growth and development of infants, because information on growth and development of infants owned is obtained from the data collection done manually such as; make records and calculations to find out the condition of a toddler declared good, less, or bad. Implementation of the SMART method in Toddler's growth and development, this method can be used based on the weights and criteria that have been determined. The criteria used are based on the Anthropometric index assessment criteria. The results of the analysis are the results of ranking the greatest value to be used as the material in the decision-making process.
Analysis of Critical Factors Influencing Online Motorcycle Taxi Driver's Income Per Transaction Using Random Forest Regressor And Feature Importance Wahana, Agung; Alam, Cecep Nurul
ISTEK Vol. 14 No. 2 (2025)
Publisher : Fakultas Sains dan Teknologi UIN Sunan Gunung Djati Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15575/istek.v14i2.2338

Abstract

This study aims to identify and measure the main factors that most significantly affect the Income of Online Motorcycle Taxi Drivers Per Transaction in the gig economy sector. The Machine Learning Random Forest Regressor algorithm was used on driver transaction data. This methodology was chosen for its ability to handle the data's non-linearity and to objectively measure Feature Importance. Traditional linear regression models have limitations in these areas. The main results show the Random Forest model is highly accurate (R2 = 0.9634). It confirms the absolute dominance of distance, which accounts for 94.98% of the total predictive importance of revenue. The Total Transaction Value factor (3.82%) is a secondary predictor. Demographic variables (Age and Gender) and temporal variables (Days and Hours) together had a minimal (less than 1%) influence on fare per trip. This research concludes that the rate per driver transaction is determined almost exclusively by the platform's distance-based pricing policy. It is neutral to the characteristics of the driver. These findings recommend that platforms focus on increasing order volume and optimizing operational costs, rather than modifying base rates.
Synthesis of Hydroxyapatite Via Sol–Gel Method for Tooth Remineralization Applications Setiadji, Soni; Damayanti, Frida Aziz; Septiani, Fiona Putri; Wahana, Agung; Rosahdi, Tina Dewi
Elkawnie Vol. 12 No. 1 (2026)
Publisher : Faculty of Science and Technology Universitas Islam Negeri Ar-Raniry

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22373/ekw.v12i1.34419

Abstract

Abstract: Enamel, the outermost and most highly mineralized tissue in the human body, is composed predominantly of hydroxyapatite and is susceptible to demineralization under acidic conditions, which can lead to enamel erosion and dental caries. Due to its chemical similarity to natural tooth mineral, hydroxyapatite has been extensively investigated as a biomimetic material for enamel remineralization. This study aimed to synthesize hydroxyapatite via the sol–gel method, formulate it into a hydroxyapatite-based paste, and evaluate its remineralization effect on demineralized premolar enamel. Hydroxyapatite was synthesized through a sol–gel process involving the reaction of Ca(OH)2 and H3PO4 at 60 °C followed by pH adjustment, aging, drying, and calcination. The material was characterized using X-ray diffraction (XRD), X-ray fluorescence (XRF), and particle size analysis (PSA). The hydroxyapatite paste was applied once daily for 14 consecutive days to demineralized premolar enamel, and remineralization was evaluated using scanning electron microscopy (SEM). XRD confirmed the formation of the hydroxyapatite phase, XRF indicated a Ca/P ratio of 1.72, and PSA showed an average particle size of 344.2 nm, suggesting the presence of particle agglomeration. SEM analysis demonstrated reduced surface porosity and partial filling of enamel defects after treatment compared to demineralized enamel. This study provides a simple sol–gel synthesis and application approach for hydroxyapatite paste, offering potential relevance for future development of non-invasive enamel repair materials in dental restorative applications. Abstrak: Enamel, yang merupakan jaringan terluar dan paling termineralisasi tinggi pada tubuh manusia, tersusun secara dominan oleh hidroksiapatit dan rentan mengalami demineralisasi dalam kondisi asam, yang dapat memicu terjadinya erosi enamel dan karies gigi. Karena kemiripan kimianya dengan mineral alami gigi, hidroksiapatit telah diteliti secara luas sebagai material biomimetik untuk remineralisasi enamel. Penelitian ini bertujuan untuk mensintesis hidroksiapatit melalui metode sol–gel, memformulasikannya ke dalam bentuk pasta berbasis hidroksiapatit, serta mengevaluasi efek remineralisasinya pada enamel gigi premolar yang terdemineralisasi. Hidroksiapatit disintesis melalui proses sol–gel yang melibatkan reaksi antara Ca(OH)2 dan H3PO4 pada suhu 60 °C, diikuti dengan penyesuaian pH, aging (penuaan), pengeringan, dan kalsinasi. Karakterisasi material dilakukan menggunakan XRD, XRF, dan PSA. Pasta hidroksiapatit diaplikasikan sekali sehari selama 14 hari berturut-turut pada enamel gigi premolar yang telah terdemineralisasi, dan proses remineralisasinya dievaluasi menggunakan SEM. Hasil XRD mengonfirmasi terbentuknya fase hidroksiapatit, analisis XRF menunjukkan rasio Ca/P sebesar 1,72, dan PSA menunjukkan ukuran partikel rata-rata sebesar 344,2 nm yang mengindikasikan adanya aglomerasi partikel. Analisis SEM menunjukkan penurunan porositas permukaan dan pengisian sebagian pada cacat enamel setelah perlakuan dibandingkan dengan enamel yang terdemineralisasi. Penelitian ini menyediakan pendekatan sintesis sol–gel dan aplikasi yang sederhana untuk pasta hidroksiapatit, serta memberikan relevansi potensial bagi pengembangan material perbaikan enamel non-invasif di masa depan dalam aplikasi restorasi gigi.
Co-Authors Adelia Irawan, Febby Cecep Nurul Alam, Cecep Nurul Damayanti, Frida Aziz Lillah, M. Rival Ridautal Lillah Maylawati, Dian Sa'adillah Rialdy Atmadja, Aldy Rohmah, Siti Nur Santosa, Dwi Bagia Septiani, Fiona Putri Soni Setiadji Tina Dewi Rosahdi, Tina Dewi Wildan Budiawan Zulfikar Wisnu Uriawan, Wisnu