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SISTEM PAKAR DIAGNOSIS PENYAKIT KULIT MENGGUNAKAN METODE FORWARD CHAINING DENGAN PYTHON Siahaan, Ranty Deviana
Jurnal Informatika dan Teknik Elektro Terapan Vol 12, No 3S1 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3S1.5088

Abstract. Skin is the outer layer of the body that functions to protect the inside. Like other parts of the body, the skin is also susceptible to disorders or diseases. Currently, the need for healthy skin is increasing due to pollution, free radicals and hot weather. Increased awareness about the importance of healthy skin encourages people to pay more attention to skincare. However, delays in identifying the type of disease and lack of knowledge about prevention can lead to more severe skin conditions, such as cancer. An expert system is an information system that utilizes knowledge and decision-making methods from experts in a particular field. Therefore, the author developed a system to help people with early diagnosis of skin diseases using the forward chaining method and the Python programming language. This implementation succeeded in creating an expert system capable of diagnosing skin diseases using the forward chaining method and Python. Based on testing each existing rule, the expert system succeeded in providing a diagnosis, description, solution or medicine, as well as a picture of the disease based on the symptoms reported by the user.

Enhancing Real-time Herbal Plant Detection in Agricultural Environments with YOLOv8 Siahaan, Ranty Deviana; Pardede, Herimanto; Simbolon, Iustisia Natalia; Simbolon, Ivanston; Gultom, Dian Jorgy
Journal of Information System and Informatics Vol 6 No 4 (2024): December
Publisher : Universitas Bina Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51519/journalisi.v6i4.889

The detection of herbal plants plays a crucial role in the utilization of traditional medicine, particularly in the Toba region of Indonesia. This study aims to develop an Android application capable of real-time detection of herbal plants using the YOLOv8 algorithm. The five types of herbal plants targeted in this study are tempuyung, rimbang, papaya leaves, turmeric leaves, and aloe vera. The research methodology includes the collection of a dataset of herbal plant images, which were then labeled using the Roboflow platform. The YOLOv8 model was trained with this dataset to detect herbal plant objects. After training, the model was exported to TensorFlow Lite and integrated into an Android application. Testing was conducted to evaluate the accuracy and real-time detection performance of the application. The results show that the YOLOv8 model achieved a mean Average Precision (mAP) of 92.4%, with optimal real-time detection capabilities on Android devices. The developed application can quickly and accurately detect and identify herbal plants, providing a practical solution for users to recognize herbal plants. This study indicates that the YOLOv8 algorithm is effective for herbal plant recognition applications in a mobile context, opening up opportunities for further development in the integration of AI technology into everyday applications.

Development of mobile-based Batak script recognition application using YOLOv8 algorithm Simbolon, Iustisia Natalia; Herimanto, Herimanto; Siahaan, Ranty Deviana; Lumbantobing, Samuel Adika; Br Sitepu, Grace Natalia
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 2: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i2.pp1013-1026

The Batak people are one of the ethnic groups that pass down many values and traditions to each generation, including the written tradition known as the Batak script. The Batak Toba people, in particular, have the Batak Toba script as part of their local wisdom that needs to be preserved and maintained. However, the use of the Batak script has significantly declined in the current era. To prevent the loss of this heritage, preservation through technology is necessary. This research utilizes a deep learning approach using the YOLOv8 algorithm to detect images of script objects, provide the coordinates of the script locations, and perform object recognition based on the dataset. The final result of this research is an Android-based application that can detect the Batak Toba script in real time and upload images. The research process involves experiments on several hyperparameters, such as epochs with a value of 200, confidence threshold, and IoU with a value of 0.5. The model evaluation shows excellent results, with a precision of 0.945, recall of 0.902, mAP@0.5 of 0.954, and a high confidence score from the application's detection.

Equivalence partitioning and cognitive walkthrough testing on the training prama website Siahaan, Ranty Deviana; Martua Panggabean, Risky Junior
Jurnal Mantik Vol. 9 No. 2 (2025): August: Manajemen, Teknologi Informatika dan Komunikasi (Mantik)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/mantik.v8i6.6399

The Prama Training Website, developed by PT. Triputra Karya Lestari, aims to support employee skill development. However, the platform is underutilized due to usability issues and non-functional features. To address these problems, a two-pronged evaluation approach was conducted: Functional Testing using the Equivalence Partitioning (EP) method, and Non-Functional Testing through Cognitive Walkthrough (CW). Testing was carried out in two iterative stages. In stage one, 127 issues were identified 31 from EP and 96 from CW, while stage two revealed 12 remaining issues. These findings informed the design of a high-fidelity prototype, which incorporated targeted improvements to interface functionality and usability. The development culminated in the implementation of a revised Front-End Final Prototype, providing a more intuitive and accessible user experience for employees

SISTEM PAKAR DIAGNOSIS PENYAKIT KULIT MENGGUNAKAN METODE FORWARD CHAINING DENGAN PYTHON Siahaan, Ranty Deviana
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 3S1 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3S1.5088

Abstract. Skin is the outer layer of the body that functions to protect the inside. Like other parts of the body, the skin is also susceptible to disorders or diseases. Currently, the need for healthy skin is increasing due to pollution, free radicals and hot weather. Increased awareness about the importance of healthy skin encourages people to pay more attention to skincare. However, delays in identifying the type of disease and lack of knowledge about prevention can lead to more severe skin conditions, such as cancer. An expert system is an information system that utilizes knowledge and decision-making methods from experts in a particular field. Therefore, the author developed a system to help people with early diagnosis of skin diseases using the forward chaining method and the Python programming language. This implementation succeeded in creating an expert system capable of diagnosing skin diseases using the forward chaining method and Python. Based on testing each existing rule, the expert system succeeded in providing a diagnosis, description, solution or medicine, as well as a picture of the disease based on the symptoms reported by the user.

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