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Jurnal Algoritma
Published by Institut Teknologi Garut
ISSN : 14123622     EISSN : 23027339     DOI : https://doi.org/10.33364/algoritma
Core Subject : Science,
Computer Science & IT
Jurnal Algoritma merupakan jurnal yang digunakan untuk mempublikasikan hasil penelitian dalam bidang Teknologi Informasi (TI), Sistem Informasi (SI), dan Rekayasa Perangkat Lunak (RPL), Multimedia (MM), dan Ilmu Komputer (Computer Science).
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 1,026 Documents
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Evaluasi Algoritma Deep Learning dan Segmentasi Klasik untuk Estimasi Luas serta Nilai Konstanta Daun Pepaya pada Sistem Smart Garden Pratama Putra, Muhammad Ali; Fachrie, Muhammad
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.2996

Abstract

Otomatisasi pada smart garden mulai membutuhkan pemantauan pertumbuhan tanaman yang akurat dan efisien, dengan pengukuran luas daun sebagai salah satu parameter yang penting. Sebuah sistem segmentasi citra dikembangkan untuk menentukan luas dan mengidentifikasi konstanta daun menjari, khususnya tanaman pepaya (Carica papaya L.). Kinerja berbagai algoritma dianalisis secara komprehensif, meliputi segmentasi klasik, machine learning dengan Random Forest Classifier, dan deep learning dengan arsitektur U-Net serta YOLOv11-seg. Pengujian menyeluruh dilakukan dengan mengevaluasi skor IoU, akurasi, recall, dan presisi untuk kinerja segmentasi; nilai RMSE dan MSE untuk akurasi prediksi nilai konstanta; serta waktu pelatihan dan inferensi untuk efisiensi komputasi setiap model. Hasil menunjukkan bahwa model YOLOv11-seg memberikan kinerja segmentasi paling stabil secara keseluruhan, dengan capaian IoU yang tinggi dan konsisten pada objek daun (0.904) dan koin (0.883). Meskipun demikian, untuk tugas identifikasi nilai konstanta, Random Forest memberikan akurasi prediksi terbaik yang ditunjukkan dengan nilai RMSE terendah 0.016. Hasil ini mengonfirmasi potensi signifikan dari metode machine learning dan deep learning untuk diterapkan dalam sistem pertanian digital, guna mendukung otomatisasi pemantauan pertumbuhan tanaman secara presisi.
Analisis Pola Konsumsi Air Menggunakan Algoritma Random Forest Classifier Pada Distribusi Air Bersih Desa Rempoah Baturraden Ngarifatul Khofiyah, Salma; Hamdi, Aulia; Nur Isnaini, Khairunnisak
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.3001

Abstract

Clean water is a basic necessity for communities that must be managed efficiently to ensure its distribution remains equitable and sustainable. In Rempoah Village, water usage is still recorded manually, making it difficult to analyze consumption patterns and detect irregular usage. This study aims to analyze water consumption patterns using the Random Forest Classifier algorithm as an effort to support clean water distribution management by the Berkah Maju Bersama Village-Owned Enterprise (BUMDes). The research data was obtained through observation and interviews with the management, followed by a data preprocessing stage that included data cleaning, missing value handling, data exploration, label encoding, and data division into training and test data. The Random Forest model was used to classify water consumption patterns into three categories, namely economical, normal, and wasteful. The results showed that the model was able to classify the data with an accuracy rate of 100%, where all test data was correctly identified. Based on the analysis results, most customers were in the wasteful category at 56.2%, indicating the need for an evaluation of the efficiency of household water use. These findings prove that the application of machine learning methods can be an effective solution in supporting decision-making and clean water management at the village level in a sustainable manner.
Optimasi Algoritma Pemilihan Soal pada POMDP Berbasis Advantage Actor-Critic untuk Model Ujian Adaptif Anggriani, Epri; Setyo Utomo, Fandy
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.3004

Abstract

Evaluation in learning through assessment plays an important role as a measure of success and assesses student competency achievement. In this context, CAT as an IRT-based adaptive assessment solution has been widely used, but has technical limitations such as heuristic question selection, dependence on question banks, and being undimensional. In addition, to solve decision-making problems in the context of adaptive testing, a general approach that can be used is policy-based reinforcement learning, such as policy gradient, particularly the REINFORCE algorithm. However, this algorithm has limitations such as high gradient variance and lacks a state-value function evaluation mechanism, making it unable to provide direct feedback on the quality of the actions taken. The purpose of this study is to optimize adaptive decision making in the POMDP framework using the Advantage Actor-Critic (A2C) algorithm, one of the Reinforcement Learning approaches. The actor generates a question selection policy based on the belief state of the NCDM model, while the critic evaluates the quality of actions to maximize cumulative rewards. The results show that in an adaptive environment, A2C performs better than the baseline, with an accuracy of 0.952 and an average reward of 18.56 in 20-question episodes, and an accuracy of 0.934 and a reward of 22.58 in 25-question episodes. In contrast, the baseline only achieved an average accuracy of around 0.789 and 0.760 in the 20 and 25 question episodes, and a reward of 14.19 and 16.80 in the 20 and 25 question episodes. The results of the study show that optimization with A2C can improve the personalization of exam question selection. This study contributes to the development of a more effective adaptive exam model, while also opening up opportunities for further research.
Tinjauan Literatur Sistem Rekomendasi Film: Mengidentifikasi Pendekatan Terbaik Febrianti, Rizkia Saski; Ningrum, Sekar Ayu; Triyono, Gandung
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.3011

Abstract

The recommendation system is a crucial element in various digital platforms, particularly within the entertainment industry. Its presence helps users discover films that align with their preferences. As the popularity of digital platforms continues to rise in the modern era, the main challenge lies in meeting users’ needs for relevant recommendations amid the diversity and ever-increasing volume of available content. This study focuses on a literature review to determine the most suitable methods to be applied in movie recommendation systems. The urgency of this research lies in the importance of a platform’s ability to provide recommendations that are not only relevant but also capable of enhancing user engagement and satisfaction. The proposed solution in this study involves applying methods that can analyze user preferences and behavior to improve the accuracy and level of personalization within the recommendation system. The research employs the Systematic Literature Review (SLR) method by collecting articles published between 2020 and 2024 from the Google Scholar database, all of which are relevant to the topic of movie recommendation systems. From the search results, 20 selected articles were used as the basis for analysis. Based on the analysis of these articles, it was found that up until the end of 2024, the most widely used method in movie recommendation systems is Collaborative Filtering, achieving the highest precision rate of 89% and a recall value of 96%.
Deteksi Komentar Spam Judi Online Berbahasa Indonesia Menggunakan XGBoost dan TF-IDF Arrayyan, Dzakwan Rafi; Guntara, Rangga Gelar; Nugraha, Muhammad Rizki
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.3012

Abstract

The phenomenon of online gambling continues to show growth with increasingly worrying trends. One of the challenges faced is the proliferation of gambling promotional comments on the YouTube platform due to the suboptimal performance of spam detection systems in recognizing manipulative language patterns. To address this issue, this study proposes a model for detecting spam comments in Indonesian using a combination of Term Frequency–Inverse Document Frequency (TF-IDF) and Extreme Gradient Boosting (XGBoost). The dataset contains 10,220 YouTube comments that have been manually labeled and processed through preprocessing stages, including unicode normalization and cleaning of irrelevant characters. The model was evaluated using 20% of the test data and produced an accuracy of 91%, precision of 92%, recall of 91%, and an F1-score of 91%. These results show that the combination of TF-IDF and XGBoost is effective for classifying short texts in YouTube comments. Thus, this study contributes to the development of Indonesian-language spam comment detection models, which are still rarely researched, and can also be used as a reference for media platforms in improving the effectiveness of stopping the spread of illegal content through social media comment sections.
Peningkatan Akurasi Rekomendasi Film Menggunakan Neural Collaborative Filtering dengan Arsitektur RecommenderNet Sukmana, Dimas; Guntara, Rangga Gelar; Nugraha, Muhammad Rizki
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.3013

Abstract

The rapid growth of the film industry and streaming platform users has given rise to the challenge of information overload, where users find it difficult to find films that suit their preferences amid the abundance of content choices. This study aims to develop a Neural Collaborative Filtering (NCF)-based movie recommendation system model with a RecommenderNet architecture to improve prediction accuracy and personal recommendation relevance. The model was evaluated using the Root Mean Square Error (RMSE) metric to assess rating prediction accuracy and Normalized Discounted Cumulative Gain (NDCG@100) to measure recommendation quality and order. The results show that the model achieves an RMSE of 0.1946 and an NDCG@100 of 0.8136, indicating the model's ability to learn user preferences and generate relevant and well-ordered recommendations. This research contributes to the development of more effective and personalized recommendation systems in the digital streaming domain and offers an efficient approach to reducing the impact of information overload and improving the user experience.
Pengembangan IoT Playground untuk Peningkatan Pemahaman Konsep IoT pada Siswa Sekolah Dasar Nurhadiansyah; Qudwatullathifah, Ratri Nuryani; Alfairisi, Mohammad Ibnu Ayip; Roainah, Falamah
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.3015

Abstract

This study aims to develop an IoT Playground as an interactive demonstration system that supports technology-based foundational learning. The product developed, called KERETA (Kendali Energi Rumah Edukatif Terpadu Otomatis), is a smart-lamp prototype in the shape of a train head designed to increase elementary students’ interest in learning through contextual and exploratory approaches. The system utilizes a NodeMCU ESP8266 microcontroller integrated with the Blynk application to wirelessly control and monitor the device via the internet. The research employed the ADDIE development model (Analysis, Design, Development, Implementation, Evaluation) with a limited trial involving 44 elementary school students. Practicality test results showed positive responses from teachers (89.5 percent) and students (86.7 percent), indicating that the tool is easy to use and effective in enhancing students’ understanding of basic Internet of Things (IoT) concepts. Effectiveness analysis using a paired t-test revealed a significant increase in students’ knowledge and learning participation after using the tool. The product functions not only as an interactive learning medium but also as a means of fostering digital literacy among young children. The IoT Playground KERETA innovation is expected to serve as a foundation for supporting the Indonesia Emas 2045 vision through the integration of technology in primary education.
Rancang Bangun Buku Tamu Digital Berbasis Semantic Web dengan Metode Waterfall Retno Aoktaviani, Bunga Dea; Wibowo, Agung
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.3018

Abstract

The advancement of technology has driven progress across various professional fields, including the administrative activities of the Indonesian Midwives Association (IBI). One of the IBI branch offices in Semarang Regency still records visitor data manually, which poses several risks such as difficulties in retrieving archives, physical damage to logbooks, and reduced administrative efficiency. This study was conducted to provide a solution to these issues. The research employed the Waterfall method, consisting of analysis, design, implementation, and testing stages. The digital guestbook system was developed as a web-based application using PHP (Hypertext Preprocessor) and a MySQL database. The integration of semantic web technology enables more advanced data processing to support analysis and decision-making. Based on eleven test scenarios using black-box testing, the system achieved 100% validity. Furthermore, data search time improved significantly—by approximately 80–90% compared to traditional manual recording. The developed application has proven effective in increasing efficiency in managing visitor data.
Kalibrasi Regresi Linier untuk Peningkatan Akurasi Load Cell pada Kursi Roda Cerdas Hakim, Muhamad Nauval; Miftahul Ashari, Wahid; Kuswanto, Jeki
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.3023

Abstract

Smart wheelchairs are an innovation designed to facilitate user mobility while monitoring their condition in real time. One of the main features developed is an integrated weight reading system. However, the accuracy of the sensor is still affected by sitting posture, body position, and surrounding environmental conditions. This study aims to improve the accuracy of the weighing system on smart wheelchairs by applying linear regression analysis as a sensor calibration method. Data collection was conducted under four conditions of use, namely sitting upright, sitting tilted, walking while sitting upright, and walking while sitting tilted, which represent variations in user load distribution. The calibration model was constructed using the average sensor reading data and evaluated using the R², MAE, and MAPE parameters. The results showed a significant improvement in accuracy with an R² value of 1.0000, MAE of 0.0687 kg, and MAPE of 0.111%, as well as a decrease in the average error from ±1.2 kg to ±0.07 kg after the calibration process. The linear regression method proved to be effective in improving the accuracy of sensor readings with light computational calculations. This study also demonstrates the potential of linear regression as an efficient lightweight calibration method for IoT-based medical systems, particularly on devices such as ESP32 or Arduino that display real-time, high-precision body weight measurements.
Model Analisis Sentimen Pengguna Aplikasi Belanja Online Menggunakan Metode Bidirectional Encoder Representations from Transformers (BERT) Prameswari, Fla Desya; Wahyono, Teguh
Jurnal Algoritma Vol 22 No 2 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-2.3035

Abstract

Dalam era digital saat ini, aplikasi belanja online telah menjadi bagian dari kehidupan sehari-hari. Ulasan dan komentar pengguna yang tersedia di platform Aplikasi Shopee menjadi sumber penting untuk memahami kepuasan pengguna. Analisis sentimen merupakan metode yang digunakan untuk memahami dan mengevaluasi opini yang diungkapkan dalam teks. Dalam penelitian ini menggunakan metode Bidirectional Encoder Representations from Transformers (BERT) untuk penerapan model analisis sentimen pada aplikasi Shopee yang merupakan sebuah platform e-commerce terpopuler di Indonesia. BERT adalah model NLP (Natural Language Processing) canggih yang menggunakan pendekatan bidireksional untuk memahami konteks kata dalam teks. Model ini dirancang untuk melakukan analisis teks dari dua arah, baik dari kiri ke kanan maupun sebaliknya, sehingga memungkinkan pemahaman menyeluruh terhadap teks yang dianalisis. Dengan menggunakan BERT, kami dapat menganalisis ulasan dan komentar pengguna dengan tingkat akurasi yang tinggi untuk mengevaluasi kepuasan pengguna. Penelitian ini melibatkan scraping data, cleaning data, labelling dan visualisasi data, splitting data, tokenisasi dan encoding data. Penelitian ini bertujuan untuk mengembangkan dan menerapkan model BERT dalam analisis sentimen pengguna aplikasi Shopee, serta meningkatkan kualitas layanan dan produk yang disediakan oleh aplikasi Shopee, sehingga dapat mempertahankan dan meningkatkan kepuasan pengguna. Hasil penelitian ini diharapkan dapat memberikan wawasan bagi pengembangan aplikasi Shopee dan perusahaan e-commerce lainnya mengenai pemanfaatan teknologi NLP untuk memahami dan meningkatkan pengalaman pengguna.

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