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All Journal Jurnal Informasi dan Teknologi TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Jurnal Ilmu Komputer dan Sistem Informasi
Khairul Fajri Ilahi
Universitas Riau
Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Library & Information Science

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Analisis Penyakit Gigi Molar dengan Menggunakan Metode Forward Chaining Khairul Fajri Ilahi
Jurnal Informasi dan Teknologi 2023, Vol. 5, No. 1
Publisher : SEULANGA SYSTEM PUBLISHER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/jidt.v5i1.229

Abstract

Molar teeth or what are often referred to as molars are the teeth that are most often used in the process of digesting food before it enters the stomach. This also makes molar teeth one of the most common diseases suffered by the community. This is also accompanied by low public knowledge about molar teeth. The number of people who suffer from molar tooth disease is not proportional to the availability of dental experts, causing all patients who want to diagnose their disease to be unserved. The method used in this research is the Forward Chaining method. The purpose of this study was to test the Forward Chaining method in analyzing molar disease and provide an initial diagnosis of molar disease so that the diagnosis generated by the system can be used as a reference for consultation with a dentist. The data used in this study were data on symptoms and diseases of molar teeth taken from the Munggu Dental Care dental clinic, the data obtained in the form of 29 symptom data and 7 data on molar tooth disease. The data obtained is then processed using the Forward Chaining inference method or search from the front. Then the patient will be asked to choose the symptoms they feel and the system will diagnose the patient's disease based on the symptoms entered by the patient. The data that enters the system will then be matched with the existing rules in the database so that an initial diagnosis will be generated. The result of this research is the Forward Chaining method designed with the PHP programming language using the Codeigniter framework is able to analyze diseases of the molar teeth very accurately. Therefore, this research can be used as a reference for further research and the resulting expert system can be used by users who want to diagnose molar tooth disease before going to the dentist
Perancangan Sistem Informasi Penyewaan Karangan Bunga pada Delima Florist Menggunakan Metode Prototype Khairul Fajri Ilahi; Boy Sandy Dwi Nugraha H.; Herlina H.
TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi Vol 6 No 1 (2026): TAMIKA: Jurnal Tugas Akhir Manajemen Informatika & Komputerisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/tamika.Vol6No1.pp46-56

Abstract

Delima Florist is a company engaged in flower board rental services, currently focusing on providing flower board rentals in Padang. At present, Delima Florist is able to serve flower board orders from customers both within Padang and from outside the city. Currently, the flower board ordering process at Delima Florist is still carried out conventionally by using a communication application, after which the orders are manually recorded in an order ledger. This has led to several serious issues, such as customer orders frequently not being properly recorded, resulting in the flower boards not being produced by the Florists. Another issue is the frequent loss of flower boards after the rental period, caused by customers failing to return them to Delima Florist. The purpose of developing this flower board rental information system is to automate the processes of ordering, production recording, and flower board returns. This system is expected to make it easier for staff to monitor orders, deliveries, and returns of flower boards. The flower board rental information system will be developed using the Prototype method, while the process flow and system design will utilize Unified Modeling Language (UML).
Integrasi Algoritma DBSCAN Dengan Sistem Informasi Geografis Untuk Mengidentifikasi Cluster Wilayah Rawan Kebakaran Provinsi Riau Chairun Nas; Khairul Fajri Ilahi; Boy Sandy Dwi Nugraha
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 5 No. 2 (2025): Mei 2026
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v5i2.386

Abstract

Riau Province is one of the regions in Indonesia that is prone to forest and land fires (karhutla), especially during the dry season. Accurately identifying the distribution patterns of fire-prone areas is crucial in supporting disaster mitigation and management efforts. This study aims to identify the spatial patterns (Cluster) of forest and land fire-prone areas (karhutla) in Riau Province using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm integrated with a Geographic Information System (GIS). This study uses NASA-MODIS data taken from 2020 to 2024 with 840 data records. The analysis results show that DBSCAN is able to effectively group hotspots, with Cluster 2 being the largest cluster covering 297 karhutla points in Bengkalis, Rokan Hilir, and Dumai. The large number of points in this cluster is due to the high frequency of forest and land fires between 2020 and 2024. However, Cluster 7 shows the best density quality with a Silhouette Coefficient value of 0.872, surpassing Cluster 2 which has a value of 0.638. The overall average Silhouette Coefficient value is 0.683, indicating that the cluster modeling is quite optimal. A total of 57 hotspots are categorized as noise, but still provide a picture of the distribution of forest and land fires. GIS-based map visualization reveals that most fire hotspots are located in peatlands and dry vegetation areas that are consistent from year to year. The results of the study confirm that the use of appropriate DBSCAN parameters (epsilon and minPts) produces accurate spatial visualization and supports more effective and targeted mitigation strategies and fire monitoring based on priority areas.    
Co-Authors Boy Sandy Dwi Nugraha H. Chairun Nas Herlina H.