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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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Optimalisasi Parameter Support Vector Machine dengan Algoritma PSO untuk Tugas Klasifikasi Sentimen Ulasan IMDb Adrian, Mochammad Ilham; Laksana, Eka Angga
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2306

Abstract

In this study, the LinearSVC algorithm from the Support Vector Machine (SVM) method was used for sentiment analysis on IMDb review data. Feature extraction was carried out using the TF-IDF Vectorizer. The main challenge lay in determining the value of the hyperparameter, particularly the regularization parameter (C), which greatly influences the quality of the prediction results. To address this issue, the study employed the Particle Swarm Optimization (PSO) algorithm to find the optimal value of C. Experiments showed that without optimization, the SVM model achieved an accuracy of only 89.48%, but after applying PSO, the optimal C value of 0.1612 was found, which increased the model’s accuracy to 92.03% on the test data. Additionally, other evaluation metrics also showed significant improvements, with a Precision of 91.29%, Recall of 92.92%, and F1-Score of 92.10%. The significance of this improvement indicates that the PSO method consistently outperforms conventional approaches that rely on manual hyperparameter selection or grid search, which are often slower and less accurate in finding the optimal value. The results of this study demonstrate that hyperparameter optimization using PSO can significantly enhance SVM performance in sentiment classification. This approach is not only relevant for analyzing IMDb reviews but can also be applied to various other NLP tasks, such as public opinion analysis and product reviews, making it an efficient solution for improving text classification accuracy.
Klasifikasi Varietas Kopi Berdasarkan Kondisi Tanah dan Suhu Menggunakan Algoritma Gaussian Naïve Bayes Riza, Fahrur; Safwandi, Safwandi; Fadlan, Said
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2311

Abstract

This research aims to classify the most suitable coffee varieties grown in the Bener Meriah region based on environmental factors such as soil pH, altitude from sea level (masl), and temperature. The method used is Gaussian Naïve Bayes, a probability-based classification technique that assumes that the input features are normally distributed and mutually independent. This method is relevant because it is able to handle numerical data efficiently. The results showed that the classification accuracy reached 45.7%, with a high precision value in the Gayo 3 class of 0.60%. Although the results are not yet optimal, the method shows potential in predicting the suitability of coffee varieties based on the analyzed environmental parameters.
Model Regresi Linear Berganda untuk Prediksi Tingkat Pengangguran di Provinsi Jawa Barat Halif, Jenny; Wahiddin, Deden; Sanjaya, Iman; Faisal, Sutan
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2312

Abstract

The Open Unemployment Rate (TPT) in West Java has been the highest nationally in recent years. This study aims to predict the TPT in 2025 using the Multiple Linear Regression (RLB) algorithm with variables such as inflation, GRDP, HDI, and population. Secondary data from 2013-2024 was analyzed through preprocessing, PCA, and training-test data division methods. The model was evaluated using RMSE and R-squared, with the results of RMSE 0.0148 and R² 0.5716. Multiple Linear Regression was chosen because it is able to handle many variables at once and provide a quantitative estimate of the contribution of each factor, in contrast to the individual approach which only looks at the influence of one variable separately. These results can serve as the basis for unemployment reduction policies at the regional level.
Klasifikasi Penyakit Hipertensi Menggunakan Support Vector Machine dan Naive Bayes Jannah, Malika Dakhola; Fauzi, Ahmad; Emilia, Cici; Hikmayanti, Hanny
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2316

Abstract

Hipertensi merupakan penyakit tidak menular yang dapat menimbulkan komplikasi serius seperti stroke dan penyakit jantung. Penelitian ini bertujuan untuk mengklasifikasikan status hipertensi menggunakan algoritma Support Vector Machine dan Naive Bayes. Dataset berjumlah 1.898 data pasien dari Puskesmas, yang telah melalui tahap pembersihan, normalisasi, serta pembagian menjadi data latih dan data uji. Akurasi, presisi, recall, dan skor f1 digunakan untuk mengevaluasi kinerja model. Hasil menunjukkan bahwa SVM memperoleh akurasi sebesar 95,71%, sedangkan Naive Bayes mencapai 93,37%. Temuan ini menunjukkan bahwa SVM lebih unggul dalam mengklasifikasikan status hipertensi. Meski demikian, Naive Bayes tetap layak digunakan karena kesederhanaan implementasinya. Algoritma ini menjadi pilihan alternatif yang efisien untuk klasifikasi medis yang cepat dan ringan. Temuan ini dapat digunakan sebagai basis pengembangan sistem deteksi dini untuk mendukung layanan kesehatan masyarakat.
Robot Logistik Berbasis IoT untuk Pengiriman Obat dan Monitoring Pasien Secara Otomatis Yani, Mohamad; Faricha, Anifatul; Rasmana, Susjianto Tri; Akbar, Achmad Syiham; Rizky, Khoiril; Putra, Aditya Firmanda; Syauqi, Fattah Rafif; Subayu, Achmad; Naafilaturrosyidah; Hasintongan, Ferdinand Ronald
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2318

Abstract

Kebutuhan akan otomatisasi layanan kesehatan mendorong pengembangan robot logistik berbasis Internet of Things (IoT) untuk pengiriman obat-obatan dan pemantauan pasien, khususnya pada kasus penyakit menular. Studi ini mengembangkan prototipe robot layanan rumah sakit dengan sistem navigasi differential drive dan algoritma Region-Reaching Control (RRC) guna memastikan pergerakan presisi dalam lorong sempit. Robot mendukung dua mode navigasi, yaitu manual melalui remote control dan otomatis berbasis area target. Hasil simulasi menunjukkan bahwa pendekatan RRC mampu menurunkan Mean Absolute Error (MAE) posisi hingga 0.135 m pada su`mbu x dan y, serta MAE orientasi 0.095 m pada sumbu (yaw) sebesar 0.178 rad, jauh lebih kecil dibanding kendali PD konvensional. Sistem komunikasi wireless menunjukkan waktu respon rata-rata 120–940 ms, dengan jangkauan efektif mencapai 80 meter di ruang terbuka dan 40 meter di ruang tertutup. Integrasi kamera omni-infrared dan kontrol jarak jauh memungkinkan operasional tanpa kontak langsung dengan pasien. Dengan akurasi tinggi dan fleksibilitas kontrol, prototipe ini menawarkan solusi efisien dan adaptif untuk distribusi logistik medis, sekaligus meningkatkan keselamatan tenaga kesehatan di lingkungan berisiko tinggi.
Impelementasi Metode Preference Selection Index (PSI) dalam Penentuan Finalis Putera Puteri Kebudayaan Sumatera Utara Nasution, Anggita Syahputri; Sriani, Sriani
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2319

Abstract

Kebudayaan lokal saat ini menghadapi tantangan serius akibat derasnya arus globalisasi, di mana generasi muda lebih tertarik terhadap budaya asing. Untuk mengatasi masalah ini, salah satu upaya yang dilakukan adalah menyelenggarakan kegiatan "Pemilihan Putera Puteri Kebudayaan Sumatera Utara" sebagai wadah pelestarian budaya. Namun, proses penilaian pada kegiatan ini masih dilakukan secara manual sehingga tidak efisien dan rawan kesalahan. Penelitian ini bertujuan untuk mengimplementasikan metode Preference Selection Index (PSI) dalam menentukan finalis terbaik tanpa harus menetapkan bobot atribut secara subjektif. Penelitian ini menggunakan 64 data finalis (33 putera dan 31 puteri) dengan 7 kriteria penilaian, yang semuanya bertipe benefit. Perhitungan dilakukan melalui sistem berbasis web. Hasilnya menunjukkan bahwa nilai PSI tertinggi pada kategori putera adalah 0,9979 dan pada kategori puteri adalah 0,9960, yang menunjukkan bahwa metode PSI mampu memberikan hasil peringkat yang akurat dan efisien. Sistem ini diharapkan dapat mempermudah pihak penyelenggara dalam menentukan finalis secara objektif dan terukur.
Penerapan Metode MOORA dalam Sistem Pendukung Keputusan Pemberian Beasiswa di SMK Negeri 1 Lubuk Pakam Syadda, Roro Nurul; Putri, Raisa Amanda
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2321

Abstract

Penentuan penerima beasiswa yang objektif dan transparan menjadi tantangan bagi pihak sekolah, khususnya di SMK Negeri 1 Lubuk Pakam, yang memiliki jumlah siswa yang besar dengan latar belakang dan prestasi yang beragam. Penelitian ini bertujuan untuk membangun sistem pendukung keputusan (SPK) berbasis metode MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis) dalam menentukan kelayakan penerima beasiswa. Metode MOORA dipilih karena mampu mengevaluasi alternatif berdasarkan kriteria benefit dan cost secara sistematis dan terukur. Kriteria yang digunakan dalam sistem ini meliputi prestasi akademik, kondisi ekonomi, kedisiplinan, motivasi belajar, dan potensi bakat. Sistem dikembangkan menggunakan bahasa pemrograman PHP dan MySQL dengan pendekatan pengembangan model Waterfall. Hasil implementasi menunjukkan bahwa sistem berhasil mengidentifikasi siswa yang layak menerima beasiswa dengan akurasi yang baik dan mempermudah proses seleksi oleh tim sekolah. Penelitian ini diharapkan dapat menjadi solusi praktis dan adaptif untuk mendukung transparansi dan efisiensi dalam penyaluran beasiswa di lingkungan pendidikan menengah.
Sistem Penyiraman Otomatis Berbasis IoT dengan Logika Fuzzy Sugeno untuk Pengendalian Kelembaban Tanah di Greenhouse Saragih, Khoirul Azmi; Kurniawan, Rakhmat
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2327

Abstract

Modern agriculture demands an efficient and adaptive irrigation system, especially in spinach (Amaranthus sp.) cultivation which requires optimal soil moisture for maximum growth. Manual watering is often inefficient and prone to irregularities. This study aims to design and implement an automatic watering system based on the Internet of Things (IoT) with the Fuzzy Sugeno method in a greenhouse. This system uses an ESP8266 microcontroller integrated with a soil moisture sensor, DHT11 temperature sensor, RTC module, and relay to control the water pump. In addition, an Android application was developed using MIT App Inventor connected to Firebase, allowing real-time monitoring and control of the system in three modes: manual, scheduling, and fuzzy-based automatic. The Fuzzy Sugeno method is used to determine the duration of watering adaptively based on temperature and soil moisture parameters. The test results show that this system is able to respond to changes in soil moisture with 90% accuracy and increase water use efficiency by 25% compared to the manual method. The implementation of this system is expected to increase water use efficiency and support automation in precision agriculture.
Rancang Bangun Sistem informasi Pengawasan Intern dan Pemantauan Tindak Lanjut Siap Mental Menggunakan Framework Laravel Ayupratiwi, Pramesti; Retnoningsih, Dwi; Fitriyadi, Farid
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2331

Abstract

The Klaten Regency Government is currently facing problems related to the lack of utilization of technology in supervising SKPD activities because it still uses manual methods, namely recording supervision in books or spreadsheets which causes delays, recording errors and difficulties in finding follow-up data findings. This research aims to design and build a laravel-based follow-up supervision and monitoring system for local government agencies. The system development method using Waterfall includes the stages of needs analysis, system design, implementation and testing. The results of blackbox testing show that the system functionality is in accordance with the percentage of 100% without any errors. In addition, the results of usability testing with the dimensions of usefulness, ease of use, ease of learning and satisfaction show the results of the usability level of 97% and are categorized as very feasible so that the system proves to be easy to use, easy to learn and has satisfied users. So based on these results, it is hoped that this system can help accelerate supervisory monitoring and increase the transparency of regional agency governance.
Sistem Irigasi Otomatis Budidaya Anggrek Menggunakan Metode Fuzzy K-Nearest Neighbor (FK-NN) Berbasis Internet of Things Maysandra, Adisty; Ikhsan, Muhammad
Jurnal Algoritma Vol 22 No 1 (2025): Jurnal Algoritma
Publisher : Institut Teknologi Garut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33364/algoritma/v.22-1.2335

Abstract

This study develops an automatic irrigation system for orchid plants based on the Internet of Things (IoT) using the Fuzzy K-Nearest Neighbor (FK-NN) method. This system utilizes soil moisture, temperature, and air humidity sensors to monitor environmental conditions and determine plant water needs. Sensor data is analyzed using FK-NN to determine the optimal irrigation pattern, automatically controlling the water pump through a drip irrigation system. Testing shows that the system accuracy reaches 95.23%, with good performance in classifying soil moisture conditions. This system is equipped with an Android application for remote monitoring and manual control. This study provides an efficient and accurate solution for orchid irrigation, supporting optimization of water use and increasing productivity.

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