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All Journal Sistemasi: Jurnal Sistem Informasi RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF APPLIED INFORMATICS AND COMPUTING METIK JURNAL Information System Journal (INFOS)
Arif Nur Rohman
Universitas Amikom Yogyakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Electrical & Electronics Engineering Engineering

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Sistem Rekomendasi Skincare Berdasarkan Jenis Kulit Menggunakan Content-Based Filtering dan Knowledge-Based Normalization Joy Raphaela; Arif Nur Rohman; Ika Nur Fajri
METIK Jurnal Vol. 10 No. 1 (2026): METIK Jurnal Issue Published
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/aa1yds57

Abstract

The rapid growth of the skincare industry has triggered information overload, complicating consumer decision-making particularly among Generation Z users on e-commerce platforms. Conventional Collaborative Filtering approaches are limited by popularity bias and the cold-start problem, and are unable to account for ingredient-level compatibility with individual skin conditions. Addressing this gap, this study proposes a novel Content-Based Filtering recommendation system that integrates TF-IDF and Cosine Similarity with a Knowledge-Based Normalization layer. This original framework maps informal consumer terminology into standardized dermatological categories, effectively reducing semantic inconsistency in unstructured product descriptions. Data were obtained from the Kaggle public repository (third-party extracted dataset) and underwent a validation process, yielding a final dataset of 91 skincare products. The system was evaluated using Precision@K across five skin-condition scenarios. Results yield an average Precision@5 of 0.80 (80%), with a peak cosine similarity score of 0.3606. The low absolute cosine value is attributable to TF-IDF vector sparsity in short-text descriptions, a characteristic acknowledged in prior literature. Implementation as a web application confirms the system's practical utility in guiding users toward biologically appropriate skincare choices, independent of market-trend bias.
SISTEM REKOMENDASI FILM BERBASIS CONTENT-BASED FILTERING MENGGUNAKAN NAIVE BAYES rahmad wardhani; Arif Nur Rohman
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7534

Abstract

The rapid growth of the digital film industry has resulted in a significant increase in available content, causing users to experience difficulties in finding movies that match their personal preferences. This condition highlights the need for an effective and personalized recommendation system. This study proposes a movie recommendation system based on a Content-Based Filtering (CBF) approach using the Naive Bayes algorithm to generate recommendations according to movie content characteristics and users’ preference histories. The MovieLens dataset obtained from Kaggle is used in this research. The research process includes data preprocessing, user profile construction, implementation of the Naive Bayes-based CBF model, and performance evaluation using Mean Absolute Error (MAE), Precision@5, Recall@5, and Mean Average Precision (MAP). The evaluation was conducted through ten repeated experiments with different training and testing data splits to ensure the reliability and stability of the results. The experimental results show that the Naive Bayes algorithm is able to effectively model the relationship between movie content attributes and user preferences. The evaluation achieved an average MAE of 0.368, Precision@5 of 0.82, Recall@5 of 0.11, and MAP of 0.42. The relatively high Precision@5 and MAP values indicate that the recommended movies are highly relevant and well-ranked at the top positions, while the low MAE value reflects satisfactory rating prediction accuracy. These results demonstrate that the proposed Naive Bayes-based Content-Based Filtering approach is capable of producing relevant, personalized, and stable movie recommendations, particularly in scenarios involving new or sparsely rated items.
PENERAPAN ALGORITMA NAÏVE BAYES DENGAN TEKNIK SMOTE UNTUK KLASIFIKASI SENTIMEN KURSUS ONLINE SKILL ACADEMY: APPLICATION OF THE NAÏVE BAYES ALGORITHM WITH SMOTE TECHNIQUE FOR SENTIMENT CLASSIFICATION OF SKILL ACADEMY ONLINE COURSES Arsellina Milka Martin; Arif Nur Rohman
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 11 No 1 (2026): Januari
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v11i1.7545

Abstract

The rapid growth of online learning has led to the emergence of various e-learning platforms, including Skill Academy. However, not all courses are able to maintain learner engagement, partly due to discrepancies between user expectations and the quality of the provided materials. This study aims to classify user sentiment toward course titles by applying the Multinomial Naïve Bayes algorithm combined with the Synthetic Minority Oversampling Technique (SMOTE) to address class imbalance. Data were collected through web scraping from five main public course pages, including information on course titles, prices, ratings, number of raters, release dates, and topic categories. Sentiment labels were assigned based on rating values, where ratings ≥ 4.0 were categorized as positive and ratings < 4.0 as negative. Text feature extraction was performed using the TF-IDF method. The experimental results show that the model developed without SMOTE achieved an accuracy of 89.36% but completely failed to identify the negative class, as indicated by a recall value of 0%. After applying SMOTE to the training data, the recall for the negative class increased significantly to 64% demonstrating a substantial improvement in the model’s ability to detect previously overlooked negative sentiment. Although a slight decrease in accuracy was observed in several testing scenarios, the improvement in recall and F1-score for the minority class represents the primary contribution of this study. These findings confirm that SMOTE is effective in mitigating class imbalance and enhances sentiment analysis performance for short text data on online course platforms.
Implementation of the Apriori Algorithm for Clothing Store Product Recommendations based on Sales Transaction History M Ilham Saputro; Arif Nur Rohman
SISTEMASI Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5648

Abstract

This study is motivated by the limitations faced by small-scale clothing stores, which generally do not have customer ratings or reviews that can be used as a basis for product recommendations. This condition necessitates an alternative method capable of utilizing available sales transaction data. The objective of this study is to generate product recommendations by identifying consumer purchasing patterns through the application of the Apriori Algorithm. The methodology involves processing sales transaction data consisting of transaction codes, lists of purchased products, and transaction timestamps. Support, confidence, and lift ratio values are calculated to generate and validate association rules among products. The analyzed data are derived from the transaction history of a clothing store and are processed using a web-based system developed with PHP and MySQL. The experimental results indicate that several product combinations achieve confidence values of 50% and lift ratios greater than or equal to 1, suggesting that these patterns can be used as a basis for product recommendations. These findings demonstrate a strong association among items that are frequently purchased together. Based on the results, this study concludes that the Apriori Algorithm is effective in identifying meaningful purchasing patterns that can support product arrangement strategies and inventory management in small-scale clothing stores.
Aceh Province Tourism Destination Recommendation System using Content based Filtering Method Ariefhan Maulana; Arif Nur Rohman; Yoga Pristyanto
SISTEMASI Vol 15, No 2 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i2.5500

Abstract

Tourists often experience difficulties in finding tourist destinations in Aceh Province that match their content preferences and are geographically close to their location. This study aims to develop a tourism destination recommendation system in Aceh Province using a Content-Based Filtering approach with the Cosine Similarity algorithm and the Haversine Formula. The dataset consists of 119 tourist destinations, including attributes such as destination name, destination description, and geographical coordinates (latitude and longitude). The research process began with text data preprocessing, which included case folding, punctuation removal, tokenization, duplicate word removal, stopword removal, and stemming. Next, the similarity between destinations was calculated using the Cosine Similarity algorithm based on tourism content descriptions, while the Haversine Formula was applied to measure the geographical distance between the user’s location and the tourist destinations. The results indicate that the developed system is able to provide relevant tourism destination recommendations by simultaneously considering content relevance and geographical proximity. Therefore, the system can assist tourists in selecting destinations that best match their preferences.
Implementation of a Hybrid Filtering Approach in a Website-based Football News Recommendation System Krissna Haridarma Putra; Arif Nur Rohman; Norhikmah hikmah
SISTEMASI Vol 15, No 1 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i1.5854

Abstract

The rapid growth of football news on digital portals has made it increasingly difficult for users to find information that matches their interests. This study develops a web-based news recommendation system by combining Content-Based Filtering and Collaborative Filtering through a feature-level Hybrid Filtering approach. The proposed hybrid approach constitutes the main novelty of this research, as it does not rely on score aggregation methods commonly used in previous studies, making it lighter, simpler, and more suitable for small datasets and limited user interactions. The system employs Term Overlap Matching to measure the similarity between news titles and Cosine Similarity to assess user preference similarity based on bookmark data. The evaluation results show that Content-Based Filtering achieves the best performance, with a Precision of 0.60, Recall of 0.75, and an F1-score of 0.67, while Collaborative Filtering performs poorly due to data sparsity in user interactions, resulting in a Precision of 0, Recall of 0, and an F1-score of 0. Overall, the feature-based hybrid approach is able to provide relevant recommendations from both content and preference perspectives, although system accuracy is still predominantly driven by Content-Based Filtering. These findings indicate that the proposed simple hybrid model can serve as an effective solution for small-scale sports news platforms and has the potential to be further improved through increased data availability, enhanced user interaction, and the adoption of more advanced NLP techniques.
PENERAPAN METODE SIMPLE ADDITIVE WEIGHTING DALAM SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN TEMPAT KOS DI CONDONG CATUR YOGYAKARTA Ridhwan Shodiq Ramadhan; Arif Nur Rohman; Alfie Nur Rahmi
Information System Journal Vol. 8 No. 02 (2025): Information System Journal (INFOS)
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/infosjournal.2025v8i02.2429

Abstract

Pemilihan tempat kos yang ideal merupakan tantangan besar bagi mahasiswa yang ingin melanjutkan pendidikannya keluar kota. Namun, proses pencarian proses penilaian fasilitas tempat kos sering kali masih harus dilakukan secara langsung, yang menghabiskan waktu dan biaya. Oleh karena itu dibangunlah sistem pendukung keputusan menggunakan metode Simple Additive Weighting (SAW) berbasis website yang dapat membantu mahasiswa dalam menentukan tempat kos sesuai dengan kriteria yang diinginkan. Metode penelitian dilakukan melalui beberapa tahap: pengumpulan data dari sumber terkait, analisis sistem untuk menyusun alur kerja, perancangan sistem rekomendasi, dan implementasi sistem. Berdasarkan hasil perhitungan perangkingan, Residence Permai terpilih sebagai rekomendasi terbaik dengan nilai akhir 0.8500, diikuti oleh Griya Seturan dengan nilai 0,8400, dan Kos Candi Gebang dengan nilai 0,8000. Hasil pengujian menunjukkan bahwa sistem ini berjalan sesuai dengan yang diharapkan dan mampu memberikan solusi yang lebih komprehensif dan praktis bagi mahasiswa yang mencari tempat kos di Condong Catur, Sleman, Yogyakarta
Content-Based Filtering Recommendation System for E-Commerce Products Using Sentence-BERT and Cosine Similarity Muhammad Abbad Raid Alfath; Arif Nur Rohman
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12920

Abstract

The rapid growth of e-commerce catalogs complicates product discovery, particularly for items with complex technical specifications like luxury watches. Conventional keyword searches and TF-IDF methods often fail to capture underlying semantic relationships. The primary objective of this study is to propose a robust e-commerce recommendation system utilizing Content-Based Filtering enhanced with Sentence-BERT (SBERT) semantic embeddings. This study employs an experimental comparative research design. The methodology involves aggregating product attributes into descriptive sentences and pre-processing them to minimize representation bias. These sentences are transformed into high-dimensional embeddings using the lightweight all-MiniLM-L6-v2 SBERT model, with similarities calculated via the Cosine Similarity algorithm. The system's performance is comparatively evaluated against a baseline TF-IDF method. Main outcomes and experimental results across 20 testing scenarios demonstrate that SBERT significantly outperformed the baseline, achieving an average Precision@5 of 93.00%, a Recall@5 of 2.71%, and a highly efficient latency of 0.38 ms. In conclusion, SBERT provides a superior, scalable solution for recommending complex products by accurately capturing the semantic similarity of technical specifications and textual representations of visual characteristics. The approach inherently resolves the item cold-start problem, and its successful integration into a web application confirms its feasibility for real-time similarity computation in modern e-commerce platforms.
Co-Authors Alfie Nur Rahmi Ariefhan Maulana Arsellina Milka Martin Fajri, Ika Nur Joy Raphaela Krissna Haridarma Putra M Ilham Saputro Muhammad Abbad Raid Alfath Norhikmah hikmah rahmad wardhani Ridhwan Shodiq Ramadhan Yoga Pristyanto