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All Journal Jurnal Informatika Terpadu Jurnal Informatika Medis (J-INFORMED)
Hidayah, Septina Alfiani
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Computer Science & IT Decision Sciences, Operations Research & Management Education Health Professions Library & Information Science Nursing Public Health Veterinary

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METODE RANDOM FOREST UNTUK MEMUDAHKAN KLASIFIKASI DIAGNOSIS PENYAKIT MENTAL Priyono, Agus; Shodiq, Muhammad; Alvinsyah, Dwi Putra; Hidayah, Septina Alfiani
Jurnal Informatika Medis Vol. 2 No. 1 (2024): Jurnal Informatika Medis (J-INFORMED)
Publisher : Program Studi Informatika Medis Universitas Muhammadiyah Muara Bungo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52060/im.v2i1.2119

Abstract

Mental health is important in the development of every individual. A bad mentality can prevent a person from developing, making a person easily stressed, hopeless, and even attempt suicide and commit crimes. Currently there are quite a lot of case related to mental health which are caused by many factors such as economic, social and medical. Reflecting on this fact, there is a need for rapid mental health detection so that immediate intervention can be carried out. This needs to be done so that the patient's condition can improve. This research focuses on diagnosing mental illness by utilizing machine learning. The method used is random forest which in several studies has been proven to produce good accuracy. Random forest performs machine learning on the attributes contained in the dataset combined with K-Fold Cross Validation so that each patient can be evaluated. Next, a tuning process is also carried out to test the parameters contained in the method. After the tuning process was carried out, the best parameters obtained were n-estimator of 30, maximum depth of 4, minimum sample leaf of 2, and minimum sample split of 10. From the combination of these parameters, accuracy is 90.83%, recall is 90.83 %, and precision of 93.25%.
Implementasi Sistem Pemesanan Hotel Menggunakan Algoritma Haversine untuk Optimalisasi Rekomendasi Lokasi Adhani, Hamka Lukmanul Hakim; Bianto, Mufti Ari; Pratama, Alif Nanda; Hidayah, Septina Alfiani
Jurnal Informatika Terpadu Vol 11 No 2 (2025): September, 2025 (On Progress)
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jit.v11i2.2030

Abstract

A location-based lodging recommendation system helps users find nearby hotels efficiently through a web-based platform. The system utilizes the Haversine algorithm to calculate the distance between the user's location and the hotel by automatically retrieving coordinates via the Geolocation API. Calculated distances are compared with hotel data stored in a MySQL database, and the results are displayed on a web interface integrated with the Google Maps API. Testing was conducted on six hotels with distances ranging from 6.73 km to 23.97 km, and results were compared with Google Maps estimates. The system achieved an average distance difference of 0.0183 km, with an accuracy rate of 99.83%. These findings indicate that the Haversine algorithm provides highly accurate distance estimations and is reliable for location-based hotel recommendation systems.
Co-Authors Adhani, Hamka Lukmanul Hakim Agus Priyono Alvinsyah, Dwi Putra Mufti Ari Bianto Muhammad Shodiq, Muhammad Pratama, Alif Nanda