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All Journal Indonesian Journal of Strategic Management Jurnal Sisfokom (Sistem Informasi dan Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar SERAMBI: Jurnal Ekonomi Manajemen dan Bisnis Islam REMIK : Riset dan E-Jurnal Manajemen Informatika Komputer JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Neraca Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Jurnal Pendidikan dan Teknologi Indonesia Jurnal Sains Teknologi dan Sistem Informasi Jurnal Pengabdian Kepada Masyarakat Jurnal Esensi Infokom: Jurnal Esensi Sistem Informasi dan Sistem Komputer Jurnal Minfo Polgan (JMP) Jurnal Manajemen Jurnal Teknologi Informasi Cyberku Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Digital Transformation Technology (Digitech) Jurnal Manajemen Informatika dan Bisnis Digital IC Tech: Majalah Ilmiah Bisma : Business and Management Journal Surya Informatika KOMPUTASI IC Tech: Majalah Ilmiah
Fenilinas Adi Artanto
Universitas Muhammadiyah Pekajangan Pekalongan
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Black Box Testing Dengan Teknik Equivalence Partitioning Pada Aplikasi MJ Autocare Artanto, Fenilinas Adi; Khambali, Ahmad; Nadifa, Shafa; Azarine, Vida Ailsa
Digital Transformation Technology Vol. 4 No. 1 (2024): Periode Maret 2024
Publisher : Information Technology and Science(ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/digitech.v4i1.4240

Abstract

Sebuah sistem informasi yang berupa aplikasi perlu dilakukan sebuah pengujian agar apabila terjadi kesalahan sistem atau yang sering disebut dengan bugs/error pada sistem dapat terdeteksi dan tidak mengganggu kegunaan dari sistem tersebut. Seperti halnya aplikasi MJ Autocare yang merupakan aplikasi kasir pada MJ Autocare. Dalam pengujian sistem informasi digunakan beberapa metode yaitu Black Box dan White Box.Pengujian Black Box lebih banyak digunakan karena pengujian Black Box langsung dilakukan pada sistem yang berjalan. Salah satu teknik dari pengujian Black Box adalah teknik Equivalence Partitioning yaitu sebuah teknik yang membagi sistem menjadi beberapa partisi test case pada input dan output dari sistem. Pada Aplikasi MJ Autocare terbagi menjadi 3 partisi test case yaitu halaman login, halaman tambah produk dan halaman laporan wallet. Pada test case input meliputi halaman login dan halaman tambah produk, sedangkan pada test case output meliputi halaman laporan wallet. Setelah dilakukan pengujian dengan menggunakan user dengan masing-masing hak akses yang diberikan didapatkan hasil bahwa sistem pada aplikasi MJ Autocare telah sesuai dengan pengembang sistem yang dibuat dalam pengujian Black Box Testing dengan teknik Equivalence Partitioning. Sistem Aplikasi MJ Autocare telah dapat berjalan sesuai dengan sistem yang dibentuk.
Analisis Sentiment Pengguna Aplikasi Mobile Banking Pada Bank Syariah Dengan Support Vector Regression Rosanti, Cholisa; Artanto, Fenilinas Adi; Saputra, Reza Edi
Jurnal Pendidikan dan Teknologi Indonesia Vol 4 No 8 (2024): JPTI - Agustus 2024
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.460

Abstract

Peningkatan layanan aplikasi mobile banking pada bank syariah yang telah tersedia di playstore dapat dilakukan dengan menggunakan analisis sentiment dengan mengumpulkan komentar dari pengguna layanan aplikasi mobile banking di playstore. Analisis ini akan memberikan wawasan tentang pengalaman penggunaan aplikasi yang dapat dijadikan acuan dalam pengembangan aplikasi. Penelitian ini menggunakan metode text mining dengan metode Support Vector Regression (SVR) karena metode SVR dapat menyelesaikan permasalahan overvitting dan memiliki tingkat akurasi yang tinggi. Data yang diperoleh pada aplikasi mobile banking Mega Syariah dan Jago Syariah, masing-masing 1.434 data dan 34.669. Hasil analisa menunjukan bahwa ulasana dari Jago Syariah lebih banyak daripada Mega Syariah. Hasil ini menunjukan bahwa basis dari pengguna Jago Syariah lebih banyak daripada Mega Syariah. Pengujian menggunakan metode SVR menunjukan nilai akurasi dari aplikasi Mega Syariah sebesar 98.12% sedangkan pada Jago Syariah mendapatkan nilai akurasi sebesar 98.18%. Hal tersebut menunjukan bahwa bahwa secara umum SVR dikatakan metode yang dapat memberikan akurasi yang tinggi pada analisis sentiment. Selain itu juga diperoleh bahwa pada aplikasi Mega Syariah menunjukan kata yang berkonotasi negatif adalah masuk dan daftar, sedangkan pada Jago Syariah kata yang berkonotasi negative adalah akun dan login. Temuan ini mengindikasikan bahwa pada kedua aplikasi tersebut perlu meningkatkan kemudahan dalam proses pendaftaran dan login dari pengguna.
Pemanfaatan Digital Marketing dengan Foto Produk dan Desain Sosial Media Fenilinas Adi Artanto; Mochamad Nasir
ABDIKAN: Jurnal Pengabdian Masyarakat Bidang Sains dan Teknologi Vol. 3 No. 4 (2024): November 2024
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/abdikan.v3i4.4355

Abstract

Efforts have been made to improve the quality of education at Vocational High Schools (SMK) with the teaching factory program (TEFA), which is a production or service-based learning method in accordance with current industry standards. One of the needs of Vocational Schools in an effort to support TEFA activities is digital marketing training using digital marketing methods in accordance with the marketing vocations found in Vocational Schools. This activity is designed to train PGRI Batang Vocational School students in product photography techniques and social media design using Canva. Activities are carried out in three stages, namely preparation, training and evaluation. The training includes an introduction to digital marketing concepts, basic photography techniques (lighting, composition, and exposure triangle), as well as social media content design using product photos. As a result, the participants were able to create product photos and design interesting social media content. One of the outcomes of this activity is the Instagram account @unique_clotes. since3202 which has been used to market TEFA products. The evaluation stage shows that this activity can provide digital marketing capabilities that can only be done with a simple tool in the form of a smartphone, so that it can support TEFA activities.
Perception of user opinions towards Sharia mobile banking applications in Indonesia based on comments on Google Play Store Rosanti, Cholisa; Artanto, Fenilinas Adi; Saputra, Reza Edi
SERAMBI: Jurnal Ekonomi Manajemen dan Bisnis Islam Vol 7 No 1 (2025)
Publisher : LPMP Imperium

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36407/serambi.v7i1.1486

Abstract

This research aims to evaluate user perceptions of the performance of m-banking applications at Islamic banks by analyzing user reviews on the Google Play Store from 4 July 2023 to 4 July 2024. The research results show that the BSI Mobile and Jago Syariah applications are the most downloaded and have the most reviews, indicating a high number of active users. Analysis using the Bag of Words (BoW) and Support Vector Regression (SVR) methods shows excellent performance in predicting user sentiment, with an accuracy of more than 90% on six sharia m-banking applications, where the Aladin Syariah application achieved the highest accuracy of 98. 54%. Research findings show that positive perceptions of the app's ease of use contribute to user loyalty. At the same time, complaints regarding the login and registration process must be improved. Positive reviews such as "good job" and "steady" dominate the BSI Mobile and Jago Syariah applications, reflecting the popularity of both applications. Public interest statement The unique features such as gold transactions on the Pegadaian Syariah application provide competitive advantages that other Sharia banks can adopt. These results provide strategic insights for Islamic banks to improve and develop their m-banking services.
Pengaruh Kualitas Produk, Citra Merek dan Harga dalam Mempengaruhi Keputusan Pembelian Smartphone Android Haifan Tri Buwono Joyo Pangestu; Fenilinas Adi Artanto
MAMEN: Jurnal Manajemen Vol. 3 No. 4 (2024): Oktober 2024
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/mamen.v3i4.4259

Abstract

This research aims to analyze factors that can influence the decision to purchase an Android smartphone  in the Pekalongan Regency area using the Partial Least Square-Structural Equation Modeling (PLS-SEM) method. The factors studied are product quality (X1), brand image (X2), and price (X3). From distributing the questionnaire, it was found that the number of respondents was 53 people who were selected using the purposive sampling method, namely Android smartphone  users in the Pekalongan Regency area. The majority of respondents obtained were male students who worked as students. The data collected was analyzed using SmartPLS 3 software. The results showed that the variables product quality (X1), brand image (X2), and price (X3) had a significantly positive influence on purchasing decisions for Android smartphone s in the Pekalongan Regency area. The R-square value obtained was 0.979, which indicated that the variables product quality, brand image and price had an influence of 97.9% in the decision to purchase an Android smartphone . Apart from that, each variable also has a P-value of less than 0.05, which shows that there is a significant relationship between these variables and the decision to purchase an Android smartphone  in the Pekalongan Regency area.
Evaluasi Metode Exponential Smoothing dan Moving Average Untuk Peramalan Data Pengangguran di Indonesia Fatkhudin, Aslam; Artanto, Fenilinas Adi; Zamaroh, Firda; Azarine, Vida Alisa
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 5 (2025): JPTI - Mei 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.640

Abstract

Pengangguran merupakan salah satu permasalahan ekonomi yang dapat mempengaruhi pertumbuhan dan kesejahteraan suatu negara. Di Indonesia tingkat pengangguran yang terus meningkat menjadi masalah yang serius dan memerlukan peramalan tingkat pengagguran yang akurat sehingga dapat dijadikan sebagai pendukung Pemerintah dalam memberikan kebijakan. Penelitian ini melakukan peramalan angka pengangguran dengan membandingkan akurasi dari metode Exponential Smoothing dan Moving Average dalam memprediksi pengangguran menggunakan data pengangguran dari tahun 1986 sampai dengan 2024 yang didapatkan dari situs website resmi Badan Pusat Statistik (BPS). Evaluasi dilakukan dengan membandingkan nilai dari Mean Absolute Error (MAE) dan Root Mean Square Error (RMSE). Dari lima metode yang di uji yaitu metode Single Exponential Smoothing, Double Exponential Smooting, Multiple Exponrntial Smoothin (Holt Winter), Singel Moving Average dan Autoregressive Integrated Moving Average (Arima), mendapatkan hasil yang menunjukan bahwa metode Double Exponetial Smoothing menjadi metode terbaik dengan menghasilkan nilai MAE sebesar 530.800 dan RMSE sebesar 707.182. Sehingga dalam melakukan peramalan Tingkat pengangguran di Indonesia disarankan menggunakan metode Double Exponential Smoothing dengan parameter nilai alpha 0,31 dan beta 0,81. Hasil peramalan yang mendekati aslinya akan memberikan hasil yang akurat yang akan memberikan kemudahan dalam pengambilan Keputusan terkait kebijakan pemerintah dalam mengatasi pengangguran di Indonesia.
Workshop Pemanfaatan Digital Marketing dalam Pemasaran Bank Sampah Fenilinas Adi Artanto
PaKMas: Jurnal Pengabdian Kepada Masyarakat Vol 5 No 1 (2025): Mei 2025
Publisher : Yayasan Pendidikan Penelitian Pengabdian Algero

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54259/pakmas.v5i1.3242

Abstract

Waste banks are one solution in overcoming environmental problems, especially in organized waste management. However, the lack of information and education about the importance of waste banks in society is a major challenge in managing waste banks. This activity aims to increase community participation in strengthening the waste bank movement by utilizing digital marketing. This activity provides a digital marketing workshop which provides training on marketing strategies through digital media and also creating social media content. In this case, Instagram social media is used as the main platform for disseminating waste bank information. Participants are taught how to create content in the form of educational content, informational content and creative content (out of the box) that can attract public attention and increase public awareness about waste banks. The result of this activity is an increase in the readiness of waste bank managers to utilize Instagram media as a suggestion for more effective information dissemination, by optimizing educational content, informational content and creative content (out of the box). This activity is expected to be able to support waste banks in managing waste more responsibly and increase community participation in the waste bank program.
Metode Support Vector Machine (SVM) dan Lexicon-Based dalam Analisis Sentiment Ulasan Pengguna Aplikasi Wink Syahrudin, Syahrudin; Fenilinas Adi Artanto; Ahmad Rifqi Maulana; Filsafat, Filsafat
JUMINTAL: Jurnal Manajemen Informatika dan Bisnis Digital Vol. 4 No. 1 (2025): Mei 2025
Publisher : Yayasan Literasi Sains Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55123/jumintal.v4i1.5236

Abstract

Sentiment Sentiment Analysis is an important method in understanding user opinions of an application. This study aims to analyze the sentiment of users of the Wink application on the Google Play Store, which is a popular application that uses Artificial Intelligence (AI) in photo and video editing that has many templates and is easy to use. Sentiment analysis was carried out using the Lexicon-Based approach and the Support Vector Machine (SVM) classification algorithm. Data was taken from reviews on the Google Play Store Wink Application which obtained 1905 review data showing that 78.6% of users gave a rating of 4 to the application, while the Lexicon-Based approach identified 69.5% of reviews with positive sentiment values ​​with many words that appear are "good", "very" and "very" where this is in accordance with the number of good ratings given also providing sentiment reviews that are mostly positive. By using the Support Vector Machine (SVM) classification algorithm, high accuracy results were obtained, namely 95% with a recall value of 1.00 and a precision of 0.93, which shows that the combination of the Lexicon-Based approach and the Support Vector Machine (SVM) algorithm is said to be effective in analyzing sentiment in Wink application reviews.
Comparison of Classification Algorithms with Bag of Words Feature in Sentiment Analysis Artanto, Fenilinas Adi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 3 (2025): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i3.2426

Abstract

The rapid growth of digital culture, especially on social media platforms, has led to the emergence of unique viral phenomena characterized by unconventional humor and illogical logic such as the Italian brainroot anomaly. Although there have been many studies on sentiment analysis, there is still a lack of studies focusing on cultural sentiment such as humor in the Italian brainroot anomaly. This study provides an overview of user sentiment analysis of the game “Hantu Tung Tung Tung Sahur 3D,” a culturally viral application anomaly italian brainroot among young people on the Google Play Store during the month of Ramadan. User reviews were collected through web scraping, and data preprocessing involved tokenization, stopword removal, lowercase, stemming, and filtering to prepare the text for analysis. Feature extraction was performed using the Bag of Words method. This study compares the performance of four widely used classification algorithms—Support Vector Machine (SVM), Naïve Bayes, Decision Tree (C4.5), and Random Forest—implemented through Orange Data Mining software, with evaluation based on K-Fold Cross Validation. The novelty of this study lies in its focus on sentiment analysis in a unique and culturally viral digital context, as well as a comparative evaluation of classification algorithms specifically on this dataset. The results show that the Random Forest algorithm achieves the highest Area Under the Curve (AUC) score of 0.529, outperforming Naïve Bayes (0.504), SVM (0.503), and Decision Tree (0.498). These findings provide new insights into the suitability of ensemble methods such as Random Forest for sentiment analysis in specific digital phenomena, highlighting its potential for more reliable sentiment classification in similar contexts.
REGRESI DENGAN EKSTRASI FITUR NEURAL BAG OF WORDS PADA ANALISIS SENTIMEN PENGGUNA APLIKASI BANK DIGITAL SYARIAH Rosanti, Cholisa; Artanto, Fenilinas Adi; Saputra, Reza Edi
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 10, No 3 (2025)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v10i3.6508

Abstract

Penelitian ini untuk menguji keefektifan metode Neural Bag of Words (NBOW) dan Support Vector Regression (SVR) dalam memprediksi sentimen pengguna aplikasi bank digital syariah. Data berasal dari ulasan pengguna di Google Play Store pada aplikasi Bank Jago Syariah, Bank Aladin Syariah, Bank Syariah Indonesia, dan Muamalat Din, dengan periode pengambilan data 4 Juli 2023 - 4 Juli 2024, menghasilkan 160,026 ulasan. Penelitian ini dimulai dengan pengumpulan, seleksi, dan pelabelan data sesuai dengan skor bintang dari ulasan, yang merupakan langkah dalam Knowledge Discovery in Databases (KDD). Setelah itu, dilakukan preprocessing dengan menghilangkan kata yang tidak relevan dan mengubah kalimat menjadi bentuk baku. Data diekstraksi dengan fitur Bag of Words (BoW) yang diimplementasikan Scikit-Learn, menghasilkan matriks frekuensi kata. lalu, data dibagi menjadi set pelatihan dan uji dengan rasio 8:2, dan model SVR dilatih dengan data pelatihan. Didapatkan hasil akurasi rata-rata 98.3%, dengan akurasi tertinggi pada data Bank Aladin Syariah (98.54%) dan akurasi terendah pada data Bank Jago Syariah (98.30%). Regresi linier menunjukkan bahwa peningkatan jumlah data berbanding lurus dengan peningkatan akurasi model NBOW, dengan rumus y = 2.10^(-7) x + 98.308. Hasil ini lebih baik dari penelitian sebelumnya yang menggunakan metode CNN+SURF, yang mencapai akurasi 84%. Temuan lain adalah kemunculan kata-kata seperti "ribet" dan "aplikasi error" dalam sentimen negatif, menunjukkan perlunya peningkatan kemudahan penggunaan dan pengoptimalan aplikasi
Co-Authors Adityo Nugroho, Adityo Ahmad Khambali Ahmad Rifqi Maulana Aini, Aulia Nurul Aini, Fadhila Nur Alamsyah, Riqi Amat Sukani Aqika Ari Yogiana Aslam Fatkhudin Aslam Fatkhudin Azarine, Vida Ailsa Azarine, Vida Alisa Cholisa Rosanti Damayanti, Ika Rizqi Dani Mutaqo Edy Subowo Eriszana Nugraha Fauzan Iryan Arzha Febrianto, M Yusuf Febrianto, M. Yusuf Febrianto, Muhammad Yusuf Filsafat, Filsafat Firdan, Mohammad Hadwitya Handayani Kusumawardani Haifan Tri Buwono Joyo Pangestu Haifan Tri Buwono Joyo Pangestu Handayani Kusumawardhani, Hadwitya Handayani, Hadwitya Haq, Muchamad Farros Ilman Hardani, Andaru Alwan Afif Himawan, Ganda Imam Rosyadi Imam Rosyadi Isna Putri Juliantono, Setyo Khakim, Amrun Khambali, Ahmad Khoirruchim, Azizi Kusumawardani, Hadwitya Handayani Kusumawardhani, Hadwitya Handayani M. Waffa Najib Hadinata M. Yusuf Febrianto M. Yusuf Febrianto Mochamad Nasir Mochamad Nasir, Mochamad muh yusuf Mundriyah Mundriyah, Mundriyah Nabil Ahmad Putra Zade Nadhifah, Shafa Nadifa, Shafa Nafilaturrosyidah, Farah Nagara, Ikrar Stya Naldi, Ricky Rey Niar Ajeng Rachmayani Nisa, Lia Khoirun Norfan Musta Dwi Nugroho, Muhammad Alfin Nuridin, Nuridin Pama, Rizkiyan Tri Ade Putra, Afif Muharram Tawakal Rachmayani, Niar Ajeng Rahmawati, Siti Elisa ramadhani, ika Riza Fahlevi Rosyadi, Imam Safli, Naufal Abiyu Saputra, Reza Edi Satrio, Teguh Setyo Juliantono Siti Elisa Rahmawati Stefanus Santosa Sukani, Amat Syahrudin Syahrudin, Syahrudin Utami, Fryda Putri Wahyu Umaedi Wahyu Umaedi Wibowo, Dimas Yanti Sri Rejeki Yogiana, Aqika Ari Zamaroh, Firda