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All Journal METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Jurnal Teknologi Terpadu IT (INFORMATIC TECHNIQUE) JOURNAL Journal of Computer Networks, Architecture and High Performance Computing Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC)
Jinan, Abwabul
Magister Of Computer Science, Potensi Utama University
Aerospace Engineering Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering

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RANCANG BANGUN APLIKASI RAMBU-RAMBU LALU LINTAS DALAM BENTUK POP QUIS BERBASIS ANDROID Abwabul Jinan; Frans Ikorasaki
IT (INFORMATIC TECHNIQUE) JOURNAL Vol 8, No 2 (2020): IT JOURNAL OKTOBER 2020
Publisher : Universitas Potensi Utama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22303/it.8.2.2020.184-198

Abstract

Saat ini perkembangan Tren Teknologi Informasi terus meningkat dengan cepat termasuk aspek pendidikan. Tidak terkecuali aspek pendidikan pada anak. Pendidikan bervariasi dan kreatif tapi yang masih mengandung unsur pendidikan cenderung dipilih oleh mereka. Hal ini dibuktikan dari sejumlah besar aplikasi atau game dengan unsur-unsur pendidikan di Android. Selama ini masih banyak yang tidak mengerti arti dari rambu-rambu lalu lintas sehingga perlu untuk mengetahui dan memahami pengertian rambu-rambu lalu lintas tersebut. Banyaknya terjadi pelangaran rambu lalu lintas di jalan raya karena minimnya pemahaman tentang peraturan dan arti dari rambu-rambu lalu lintas tersebut. Seperti seringnya terjadi menerobos lampu lalu lintas, salah parkir, salah berhenti, dan sebagainya, maka perlunya pemahaman tentang rambu-rambu lalu lintas terutama untuk anak-anak. Dengan adanya aplikasi rambu-rambu berbasis Android. Sangat berguna untuk anak-anak maupun masyarakat pada umumnya dalam proses belajar dan memahami arti dari rambu-rambu lalu lintas.
Bulldog Breed Classification Using VGG-19 and Ensemble Learning Abwabul Jinan; Zakarias Situmorang; Rika Rosnelly
Proceeding of International Conference on Information Science and Technology Innovation (ICoSTEC) Vol. 2 No. 1 (2023): Proceeding of International Conference on Information Science and Technology In
Publisher : Universitas Respati Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35842/icostec.v2i1.29

Abstract

In image classification, the C4.5, Adaboost, and Gradient Boosting algorithms need another method to extract the image's features in the classification process. This research employs transfer learning with the VGG-19 network for the image's features extraction and transfers the result as a dataset to classify image-based Bulldog breeds. As the classifier to classify the extracted features from the VGG 16 model, we employ three ensemble learning algorithms, namely C4.5, AdaBoost, and Gradient Boost. The training data classification results of the American, English, and French bulldog breeds show that, with a 20-fold cross-validation evaluation, the Gradient Boosting algorithm performs the best, with an accuracy value of 0.958, a precision value of 0.958 and recall value of 0.933. And show the highest accuracy (0.933), precision (0.938), and recall (0.933) in the testing data classification. While in the testing data classification, the Gradient Boosting algorithm scores an accuracy value of 0.933, a precision value of 0.938, and a recall value of 0.933
Comparing Neural Networks, Support Vector Machines, and Naïve Bayes Algorhythms for Classifying Banana Types Abwabul Jinan; Manutur Siregar; Vicky Rolanda; Dede Fika Suryani; Abdul Muis
Journal of Computer Networks, Architecture and High Performance Computing Vol. 6 No. 1 (2024): Article Research Volume 6 Issue 1, January 2024
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/cnahpc.v6i1.3381

Abstract

One of the most significant fruits for human consumption is the banana. Fruit consumption not only promotes health but also lowers the risk of heart disease, stroke, digestive issues, hypertension, some cancers, cataracts in the eyes, skin ailments, cholesterol reduction, and, perhaps most importantly, boosts immunity.The study included secondary data, which is information gathered from online resources like Kaggle. Ten categories of bananas will be identified from the 531 total varieties of bananas used as a train dataset: Ambon bananas, Stone bananas, Cavendish bananas, Kepok bananas, Mas bananas, Red bananas, plantains, Milk bananas, Horn bananas, and Varigata bananas. The development of information technology for image object recognition has become a very intriguing topic along with the rapid advancement of society, and it is undoubtedly directly tied to information data. In order to examine Naive Bayes, Support Vector Machine, and Neural Network techniques for classifying banana types, researchers will use the SqueezeNet Deep Learning model to extract features from photos. The study's findings will provide empirical evidence for the distinctions between each algorithm's accuracy, recall, and precision. Based on the collected results, the Neural Network (NN) method is the best in terms of classification, with accuracy of 72.3%, precision of 72.1%, and recall of 72.3%.
Implementasi Metode Case-Based Reasoning (CBR) dalam Sistem Pakar untuk Mendapatkan Diagnosis Anxiety Disorders Gunung, Tar Muhammad Raja; Lubis, Siti Sahara; Siregar, Manutur; Simanjuntak, Peter Jaya Negara; Jinan, Abwabul
Jurnal Teknologi Terpadu Vol 10 No 2 (2024): Desember, 2024
Publisher : LPPM STT Terpadu Nurul Fikri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54914/jtt.v10i2.1480

Abstract

This research aims to develop an expert system based on the case-based reasoning method for diagnosing anxiety disorders. Anxiety Disorder is a mental health disorder that is often experienced by the public but is often not detected correctly. The case-based reasoning method was chosen because of its ability to utilise previous cases to solve new problems that have similarities. Case-based reasoning uses four main stages: retrieval, reuse, revise, and retain. The case-based reasoning method is implemented using case data obtained from psychology clinics and interviews with mental health experts. Testing the case-based reasoning method shows a high level of accuracy in diagnosing various types of Anxiety Disorders, such as Generalised Anxiety Disorder, Panic Disorder, and Specific Phobias. The results of this study show that the case-based reasoning method can be an effective tool in helping mental health professionals diagnose Anxiety Disorders more quickly and accurately. After searching using the symptoms obtained, the percentage of each type of disease is the percentage of Generalised Anxiety Disorder 35.7%, the percentage of Panic Disorder 30.7%, and the percentage of Specific Phobias 65%.
Penerapan Algoritma Sorting dalam Penentuan Pekerja Pada Aplikasi Cari Kerja Oleh dan Untuk Warga Satu Kelurahan Dataran Tinggi Binjai Siregar, Manutur Pandapotan; Jinan, Abwabul; Siagian, Akbar Idaman Prince Peter S.
METHOMIKA: Jurnal Manajemen Informatika & Komputerisasi Akuntansi Vol. 8 No. 2 (2024): METHOMIKA: Jurnal Manajemen Informatika & Komputersisasi Akuntansi
Publisher : Universitas Methodist Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46880/jmika.Vol8No2.pp294-300

Abstract

The current job search process often involves posting an announcement on paper or a banner on a notice board, or in front of the company’s location. Another common method is through job search applications, such as JobStreet and others. The first method has a drawback because people may not know when the job posting is published. Meanwhile, with the second method, many people hesitate to use these applications as they feel their skills may not be sufficient. To address these issues, an Android or web-based job search application is proposed to facilitate job sharing and job seeking within a nearby area, specifically within a single subdistrict. This application is targeted at individuals with a high school education level or lower, and the jobs shared are typically daily work requiring minimal skills, such as construction work, electrical repairs, gardening, cleaning, and similar tasks. A sorting algorithm will be implemented to help select the nearest and most suitable candidate for each job. To access the application, users must first register, enabling employers to post jobs and workers to find suitable positions.
Co-Authors Abdul Muis Akbar Idaman Dede Fika Suryani Frans Ikorasaki Lubis, Siti Sahara Manutur Siregar Rika Rosnelly Simanjuntak, Peter Jaya Negara Siregar, Manutur Pandapotan Tar Muhammad Raja Gunung Vicky Rolanda Zakarias Situmorang