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All Journal Dinamik International Journal of Artificial Intelligence Research SMARTICS Journal JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Applied Information System and Management METIK JURNAL Jusikom: Jurnal Sistem Informasi Ilmu Komputer JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) JIKA (Jurnal Informatika) Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Pendidikan dan Teknologi Indonesia Djtechno: Jurnal Teknologi Informasi KLIK: Kajian Ilmiah Informatika dan Komputer Instal : Jurnal Komputer J-Intech (Journal of Information and Technology) Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science
Hilabi, Shofa Shofiah
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science

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Klasifikasi Sentimen Komentar Pengguna pada Aplikasi Ruangguru Menggunakan Algoritma Naive Bayes Yovika Aprianti; Tukino; Hananto, April Lia; Hilabi, Shofa Shofiah
METIK JURNAL (AKREDITASI SINTA 3) Vol. 9 No. 1 (2025): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v9i1.1023

Abstract

The advancement of digital technology has encouraged the increasing use of online learning applications such as Ruangguru, while simultaneously fostering various innovations in the field of education. Ruangguru, as one of the most popular educational applications in Indonesia, receives thousands of user comments that can be analyzed to reflect user satisfaction and perception. This study aims to automatically classify user comments based on the sentiments they contain using the Naïve Bayes Classifier algorithm. This approach is expected to help Ruangguru developers better understand user needs and preferences, thereby improving service quality. The dataset was obtained from the Google Play Store platform, consisting of approximately 5,000 comments collected during the period from October 28 to December 31, using the google-play-scraper tool. The application of the Multinomial Naïve Bayes algorithm with TF-IDF weighting was employed to analyze the data, resulting in four sentiment categories: Baik Sekali, Baik, Cukup Baik, and Kurang Baik. Evaluation of the model was conducted using accuracy, precision, recall, and F1-score metrics. With an accuracy rate of 84.83%, the model correctly predicted the actual labels in approximately 85% of the test data. The model also achieved an F1-score of 85%, a precision of 86%, and a recall of 85%. The classification results revealed that the “Baik” category dominated with a proportion of 28.3%, followed by “Baik Sekali” at 24.3%, “Cukup Baik” at 24.0%, and “Kurang Baik” at 23.4%. These findings indicate that the model maintains a reasonable balance between sensitivity and accuracy in sentiment classification. Therefore, the Naïve Bayes Classifier method is capable of automatically identifying user opinions and has the potential to serve as a valuable tool in sentiment analysis for online learning services.
Analisis Sentimen, Grab Indonesi Analisis Sentimen Grab Indonesia Pada Ulasan Google Play Store Menggunakan Algoritma Naïve Bayes Dan SVM Nurfauzi, Oka Muhamad; Hilabi, Shofa Shofiah; Nurapriani, Fitria; Huda, Baenil
SMARTICS Journal Vol 11 No 1 (2025): SMARTICS Journal (April 2025)
Publisher : Universitas PGRI Kanjuruhan Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/smartics.v11i1.11789

Abstract

This study uses the Naïve Bayes and Support Vector Machine (SVM) algorithms to analyze the sentiment of user reviews on the Grab Indonesia app on the Google Play Store.  Web scraping was used to gather the review data, which was then processed through a number of stages, such as tokenization, letter modification, the elimination of unnecessary words, and weighting using the TF-IDF approach.  The findings of the investigation demonstrate that SVM performs better in classifying positive and negative sentiments and has a greater accuracy (93%) than Naïve Bayes (92%).  But in terms of computational efficiency, Naïve Bayes continues to lead the field.  This study sheds light on how well both algorithms do sentiment analysis on Indonesian mobile apps
Development of Geolocation-Based Employee Attendance Application on Android Mobile Kurnia, Nisa; Hananto, April Lia; Tukino, Tukino; Hilabi, Shofa Shofiah
J-INTECH ( Journal of Information and Technology) Vol 13 No 01 (2025): J-Intech : Journal of Information and Technology
Publisher : LPPM STIKI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/j-intech.v13i01.1890

Abstract

The development of mobile-based systems in Indonesia has provided innovative solutions to improve the efficiency of conventional administrative processes, especially in employee attendance. This research aims to develop an Android-based employee attendance application that is integrated with geolocation technology to enable accurate and real-time attendance monitoring. This system is built using the Waterfall method, which includes the stages of needs analysis, system design, implementation using Flutter and Dart programming language, and testing using black box testing techniques. Black-box testing was conducted on six main functions, resulting in a 94% overall success rate. Most functions achieved a 100% pass rate, but two test cases for attendance check in/out failed due to GPS location inaccuracies, highlighting the impact of device and environmental factors. The average response time was 1.28 seconds, and the average GPS delay was 2.1 seconds. The implementation of real-time notifications and admin verification improved transparency and minimized attendance fraud. The results demonstrate that the application provides an effective and efficient solution for employee attendance management. Future work should focus on enhancing location accuracy, conducting non-functional testing, and expanding features to ensure broader adoption and system robustness.
Evaluasi Layanan Aplikasi Mobile Tangkar Menggunakan Pendekatan Human Centered Design dan System Usability Scale Shidiq, Faisal; Huda, Baenil; Hilabi, Shofa Shofiah; Tukino
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 7 (2025): JPTI - Juli 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.876

Abstract

Dinas Komunikasi dan Informatika Kabupaten Karawang mengelola sebuah Aplikasi mobile Tangkar. Namun umpan balik pengguna mengindikasikan kesulitan dalam penggunaan Aplikasi mobile Tangkar. Penelitian ini, dengan mengutamakan kebutuhan pengguna melalui pendekatan Human Centered Design. Selanjutnya, dievaluasi menggunakan System Usability Scale. Rangkaian penelitian dimulai dengan understanding and specifying the context of use, mengidentifikasi user requirements, menghasilkan solusi design solution, dan melakukan evaluating the design. Proses evaluasi ini secara khusus dilakukan pada fase specifying the user requirements dengan melibatkan 40 responden yang diolah menggunakan google colab menghasilkan skor 46,06 yang masuk ke dalam kategori Not Acceptable pada tingkat accaptablility, dengan grade F dan tingkat adjective rating masuk ke dalam tingkat poor. Berdasarkan evaluasi didapatkan 9 poin permasalahan yang dirasakan oleh pengguna Aplikasi mobile Tangkar untuk dilakukan sebuah perbaikan. Mengacu pada daftar masalah yang teridentifikasi, peneliti mengimplementasikan perbaikan design antarmuka pengguna selama fase producing design solution. Desain solusi dikembangkan melalui siklus pengulangan yang teratur, yang memungkinkan penyesuaian berkelanjutan agar sesuai dengan kebutuhan pengguna. Penelitian ini menghasilkan prototype sebagai produk akhir yang kemudian diuji ulang oleh responden yang sebelumnya telah memberikan evaluasi. Skor yang didapatkan pada evaluasi akhir ini sebesar 70,62 yang tergolong dalam kategori Acceptable pada tingkat penerimaan, dengan perolehan nilai grade C dan tingkat penilaian deskriptif yang berada dalam kategori good. Analisis lebih lanjut menggunakan hierarchical clustering dengan Ward linkage mengonfirmasi perubahan signifikan dalam pengelompokan pengguna setelah perbaikan (ARI -0.039), mengindikasikan dampak positif perbaikan terhadap persepsi kegunaan meskipun area peningkatan lebih lanjut masih diperlukan. Penelitian ini menegaskan efektivitas pendekatan Human-Centered Design dalam meningkatkan kegunaan aplikasi mobile pemerintahan dan memberikan implikasi praktis untuk pengembangannya.
KLASIFIKASI TEXT ULASAN PENGGUNA APLIKASI WONDR BY BNI MENGGUNAKAN ALGORITMA NAIVE BAYES Sari, Fitria Ratna; Tukino, Tukino; Hilabi, Shofa Shofiah; Priyatna, Bayu
Djtechno: Jurnal Teknologi Informasi Vol 6, No 2 (2025): Agustus
Publisher : Universitas Dharmawangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46576/djtechno.v6i2.6819

Abstract

Penelitian ini berfokus pada proses klasifikasi ulasan pengguna aplikasi Wondr by BNI dengan menerapkan algoritma Naïve Bayes. Data yang digunakan berasal dari platform Kaggle, terdiri dari 1.500 data ulasan pengguna aplikasi yang telah melewati tahapan pre-processing seperti cleansing, tokenization, transform cases, stopwords, dan filter tokens. Ulasan tersebut kemudian diberi label secara manual ke dalam kategori label cepat, biasa saja, lambat, dan tidak responsif. Setelah itu label akan di buat otomatis oleh Naïve Bayes. Dataset dibagi menjadi 80:20, lalu di proses menggunakan model klasifikasi berbasis probabilistik Naïve Bayes. Hasil pengujian menunjukkan bahwa algoritma Naïve Bayes mampu mengklasifikasikan ulasan pengguna dengan tingkat akurasi sebesar 95%. Evaluasi model berdasarkan precision, recall, dan f1-score menunjukkan performa klasifikasi yang sangat baik pada setiap kategori ulasan. Visualisasi hasil klasifikasi menggunakan confusion matrix, diagram batang, dan wordcloud memberikan pemahaman lebih mendalam terhadap pola ulasan pengguna. Temuan ini membuktikan bahwa algoritma Naïve Bayes efektif dalam menangani teks tidak terstruktur dan dapat diandalkan untuk mendukung analisis evaluasi layanan digital berbasis umpan balik pengguna.Kata Kunci: Klasifikasi Teks, Naïve Bayes, Ulasan Pengguna, Kaggle, Wondr by BNI
IMPLEMENTASI ALGORITMA K-NN UNTUK KLASIFIKASI PENJUALAN MENU TERLARIS DI RM RATU CHANIAGO Sari, Nurnilam; Hananto, Aprilia; Priatna, Bayu; Hilabi, Shofa Shofiah
Jurnal Informatika Vol 9, No 3 (2025): JIKA (Jurnal Informatika)
Publisher : University of Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/jika.v9i3.14445

Abstract

Perkembangan teknologi informasi telah mendorong sektor bisnis kuliner untuk memanfaatkan data dalam pengambilan keputusan strategis. RM Padang Ratu Chaniago masih menghadapi kendala dalam menentukan menu yang paling diminati oleh pelanggan karena belum adanya sistem analisis berbasis data. Penelitian ini bertujuan untuk mengklasifikasikan tingkat keberhasilan menu dengan menggunakan algoritma K-Nearest Neighbor (K-NN). Metode yang digunakan antara lain mengumpulkan data penjualan selama satu tahun, melakukan pra-pemrosesan, menerapkan algoritma K-NN dengan nilai k=3, dan melakukan evaluasi kinerja model. Hasil penelitian menunjukkan bahwa model K-NN mampu mengklasifikasikan data dengan akurasi sebesar 95.45%, rata-rata precision sebesar 0.96, recall sebesar 0.94, dan F1-score sebesar 0.95. Evaluasi melalui confusion matrix menunjukkan hanya satu kali misklasifikasi dari 22 data uji. Penelitian ini membuktikan bahwa algoritma K-NN efektif dalam membantu restoran menentukan strategi pengembangan menu dan pengadaan bahan baku secara lebih tepat sasaran dan penyusunan paket menu yang menarik bagi pelanggan.
Deep Learning Model for Automated Tire Crack Detection Using Convolutional Neural Networks Hilabi, Shofa Shofiah; Fauzi, Ahmad; Savina, Savina
Applied Information System and Management (AISM) Vol. 8 No. 1 (2025): Applied Information System and Management (AISM)
Publisher : Depart. of Information Systems, FST, UIN Syarif Hidayatullah Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/aism.v8i1.46226

Abstract

Tire cracks pose a significant safety risk, as undetected defects can lead to severe accidents. Traditional inspection methods rely on manual visual assessments, which are prone to human error. This study proposes an automated tire crack detection system using Convolutional Neural Networks (CNN), leveraging transfer learning techniques to improve accuracy and generalization. A dataset of 600 tire images was collected and preprocessed, including augmentation techniques such as rotation, flipping, and brightness adjustments. The CNN model was trained with different optimizers, including Adam and Stochastic Gradient Descent (SGD), to compare their performance. Experimental results indicate that Adam achieved the highest test accuracy of 78.3% with the lowest test loss of 53%, while SGD required more epochs to reach optimal performance. This study demonstrates the feasibility of deep learning-based automated tire inspection, providing a scalable alternative to traditional methods. Future research should focus on optimizing model architectures, expanding datasets, and integrating real-time detection for industrial applications.
Penerapan Metode Multi Factor Evaluation Process Dalam Penilaian Kinerja Karyawan Bagian Produksi CNC Muhamad Bayu Aditya Pratama; Tukino; Huda, Baenil; Hilabi, Shofa Shofiah
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 5 (2024): April 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i5.1785

Abstract

Reflecting on the problems that have occurred previously due to the company's lack of managing its human resources properly, causing chaos in the production department. This problem arises because in the company there are too many PKWT (Specified Time Work Agreement) employees working, causing instability in the production line. When PKWT's work period was about to end, many prospective replacement employees resigned during the training stage because they could not stand the pressure of working in hot room temperatures in making wheel rims. This causes the company to have to look for other candidates which will take many time, while production must continue as usual. Of course, this will have a negative impact on the company if it continues in the future. Therefore, good human resource management is needed to ensure that the Company's goals are achieved efficiently and effectively. One of the things that must be done in managing human resources is monitoring and viewing employee performance. Because by knowing the performance of the Company's employees, you can ensure that tasks are carried out to predetermined standards and achieve optimal results. Employee performance assessments are carried out in the CNC section, especially on production results. This assessment aims to assess employee performance objectively and fairly, so that it can be used as an evaluation for decision making such as contract extensions, the appointment of permanent employees, promotions, and others. This performance assessment can certainly really help companies manage their human resources and reduce the impact of company losses in the future. Apart from that, this assessment is very important for a company to ensure the creation of a good quality product. The author uses a computerized system using Python and the Multi Factor Evaluation Process method to evaluate employee performance. The reason the author uses this method is because of its ability to make precise judgments based on predetermined criteria values. The data in this research is CNC production data recorded for one month. By assessing 46 employees based on data on Total Production, OK Goods (according to SOP), and Reject Goods (Not Good). The results of implementing MFEP in this performance assessment produced the highest score of 9.2 while the lowest score was 3.0.
Blockchain Application On Independent Smart Agriculture Hilabi, Shofa Shofiah; Fauzi, Ahmad; Tukino, Tukino
International Journal of Artificial Intelligence Research Vol 7, No 1.1 (2023)
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v7i1.1.988

Abstract

The agricultural supply chain is currently facing challenges such as lack of transparency, uncertainty in product origin, and difficulty in accurately tracking products. This article discusses the application of blockchain technology as a solution to enhance agricultural supply chain management. It analyzes how blockchain can improve transparency, reliability, and security in agricultural supply chain management by recording and verifying information in a decentralized manner. Through blockchain, information such as product origin, production methods, shipping details, and storage conditions can be easily traced and verified by the involved parties. The implementation of blockchain also enables smart contracts to automatically execute agreements and payments based on predefined conditions, reducing bureaucracy and enhancing efficiency. The article also addresses challenges in implementing blockchain in the agricultural supply chain, such as data standardization and collaboration among stakeholders. By implementing blockchain technology, it is expected to create a more transparent, efficient, and trusted agricultural supply chain, benefiting farmers, producers, distributors, and consumers by ensuring product authenticity, improving compliance with quality standards, and minimizing the risks of counterfeiting or contamination.  
Pemilihan Platform Film Streaming Menggunakan Metode SMARTER dan MOORA: Selection of Streaming Film Platforms Using the SMARTER Method and the MOORA Saputri, Arini; Hilabi, Shofa Shofiah; Nurapriani, Fitria; Huda, Baenil
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 2 (2024): MALCOM April 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i2.1325

Abstract

Sektor industri perfilman telah menjadi aspek tontonan wajib dalam masyarakat, saat ini film menjadi suatu hiburan yang populer di Indonesia. Kemajuan teknologi dan digitalisasi memfasilitasi akses mudah menonton film, masa transisi dari penggunaan DVD/VCD ke Blu-Ray sebagai media untuk menikmati film yang mendapatkan daya tarik pada masanya. Perkembangan internet dan platform online yang semakin pesat telah mengubah industri dunia perfilman, banyak sekali bermunculan berbagai layanan streaming yang menawarkan kemudahan untuk menonton film kapan saja dan dimana saja. Maraknya kemudahan menonton film streaming dengan tersedianya berbagai platform film masih banyak terdapat perbedaan beberapa aspek baik tampilan maupun layanan yang ditawarkan, sehingga penelitian ini memberikan wawasan dan rekomendasi mengenai opsi streaming yang baik. Dalam penelitian ini menggunakan metode MOORA dan SMARTER Kedua metodologi menghasilkan hasil yang sebanding pada nilai tertinggi yaitu Netflik sebagai platform film streaming paling aman dengan skor 0,421 pada metode SMARTER dan 0,582 pada metode MOORA , dan mengalami selisih perbedaan yang tidak terlalu signifikah terkait peroleh nilai tertinggi kedua, Dimana pada metode SMARTER di peroleh oleh Disney Hotstar dengan nilai 0,377sedangkan pada metode MOORA nilai tertinggi kedua di peroleh oleh Iflix dengan nilai0,297sehingga kedua metode ini sangat ideal untuk digunakan.
Co-Authors Al Khadzik, Fahmi Angraeni, Rahmah Nur April Lia Hananto Desfianthy, Fatiya Hanifah Fatlun, Aulia Fauzi Ahmad Muda Hananto, Agustia Hayati, Cucu Huda, Baenil Khairunisa, Sabnabila Kurnia, Nisa Muhamad Bayu Aditya Pratama Nijunnihayah, Uktupi Novalia, Elfina Nurapriani, Fitria Nurfauzi, Oka Muhamad Pratama, Daffa Agung Priatna, Bayu Priyatna, Bayu Ramadan, Mohamad Rizqulloh Rosalina, Elsa Saputri, Arini Sari, Fitria Ratna Sari, Nurnilam savina, savina Shidiq, Faisal Sunarya, Edwin Yohanes Syafana, Vinka Tukino, Tukino tukino, tukino Yovika Aprianti Zulfiana, Rizka