Article Per Year (5 Year)
p-Index From 2021 - 2026
1.235
P-Index
This Author published in this journals
All Journal AKADEMIKA Jurnal Teknik Informatika (JUTIF) Decode: Jurnal Pendidikan Teknologi Informasi Mohuyula : Jurnal Pengabdian Kepada Masyarakat Riau Jurnal Teknik Informatika Jurnal Karya untuk Masyarakat (JKuM)
Habie, Khairul Fathan
Universitas Muhammadiyah Gorontalo
Humanities Computer Science & IT Control & Systems Engineering Education Environmental Science Languange, Linguistic, Communication & Media Social Sciences Other

Published : 7 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 7 Documents
Search

 1 
Peningkatan Cyber Security Awareness Melalui Pelatihan Edukatif Kepada Siswa Sekolah Menengah Kejuruan Habie, Khairul Fathan; Putro, Aldibangun Pidekso; Yuliansyah, Herman; Riadi, Imam
Mohuyula : Jurnal Pengabdian Kepada Masyarakat Vol 3, No 2 (2024): Desember
Publisher : Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/mohuyula.3.2.46-53.2024

Abstract

Perkembangan teknologi informasi dan komunikasi telah mengalami pertumbuhan yang sangat pesat. Era digital membawa banyak kemudahan dan keuntungan, mulai dari akses informasi yang cepat, komunikasi yang efisien, hingga kemudahan layanan online yang mempermudah kehidupan sehari-hari. Sejalan dengan hal tersebut era digital juga menghadirkan tantangan baru dalam hal keamanan informasi atau cyber security. Keamanan informasi menjadi semakin krusial dengan meningkatnya penggunaan teknologi digital, membuat individu, organisasi, dan institusi rentan terhadap ancaman siber seperti peretasan, pencurian data, malware, dan serangan phishing.  Oleh karena itu, diperlukan pelatihan dengan tujuan untuk meningkatkan kesadaran dan pemahaman siswa SMK 1 Sedayu, tentang pentingnya kesadaran dan pemahaman cyber security.  Kegiatan program pemberdayaan umat (PRODAMAT) meliputi tahap persiapan, pelaksanaan, dan evaluasi. Kegiatan PRODAMAT dilakukan dengan metode ceramah dan diskusi tanya jawab. Peningkatan pemahaman siswa diukur dengan rangkaian pre-test dan post-test. Hasil yang diperoleh dari program pemberdayaan umat menunjukkan bahwa adanya peningkatan pemahaman pada indikator kesadaran “Sangat Paham” meningkat dari 31% menjadi 52%.
Impact of Optimizer Selection on MobileNetV1 Performance for Skin Disease Detection Using Digital Images Habie, Khairul Fathan; Murinto, Murinto; Sunardi, Sunardi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4685

Abstract

Automatic detection of skin diseases using digital images is a growing field in the application of deep learning in the medical world, especially to help the early diagnosis process. One of the most widely used models is MobileNetV1 because it is lightweight and efficient in image processing. However, the performance of the model is greatly affected by the training configuration, including the type of optimizer used. This study aims to compare the effectiveness of six types of optimizers, namely SGD, RMSprop, Adam, Adadelta, Adagrad, Adamax, and Nadam in training MobileNetV1 models for human skin disease image classification. The model was trained on annotated skin image dataset with predetermined training parameters: batch size 32, learning rate of 0.0001, and 10 epochs. Performance evaluation was performed using accuracy metrics. The results obtained demonstrate that RMSprop performs best, with 99.10% accuracy, 99.14% precision, 99.10% recall, and a 99.10% F1-score. Adadelta showed the lowest performance consistently, with only 22.22% accuracy, 20.34% precision, 22.22% recall, and 18.42% F1-score. This finding confirms that the type of optimizer affects the effectiveness of model training, especially in medical image classification tasks. This research provides empirical insights that are useful in selecting the optimal optimizer for MobileNetV1 model implementation in the healthcare domain.
Klasifikasi Pegawai Terbaik Triwulan pada BPS Provinsi Gorontalo Menggunakan Algoritma Naïve Bayes Hanna Fidri Mardiny; Frangky Tupamahu; Hilmansyah Gani; Khairul Fathan Habie
Riau Jurnal Teknik Informatika Vol. 5 No. 2 (2026): Juli 2026
Publisher : Prodi Teknik Informatika Universitas Pasir Pengaraian

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30606/rjti.v5i2.4714

Abstract

The selection of the best employee is an important performance evaluation process aimed at improving employee motivation and productivity. At the Statistics Indonesia (BPS) of Gorontalo Province, the selection process still involves subjective considerations, which may affect the consistency and objectivity of decision-making. Therefore, a data-driven approach is needed to support the evaluation process. This study aimed to implement the Naïve Bayes algorithm to classify the best quarterly employee based on employee performance assessment data. The dataset consisted of performance records from 54 employees of BPS Gorontalo Province collected from the first quarter of 2023 to the fourth quarter of 2024. The classification process utilized BerAKHLAK behavioral indicators, discipline indicators, and Employee Performance Achievement (CKP) as predictor variables, while the target variable was employee status, namely best employee and non-best employee. The research stages included data preparation, data transformation, training and testing data partitioning, model development using the Naïve Bayes algorithm, and model evaluation using a confusion matrix with accuracy, precision, recall, and F1-score metrics. The evaluation results showed that the proposed model achieved an accuracy, precision, recall, and F1-score of 100%, indicating high classification performance on the dataset used in this study. These findings demonstrate that the Naïve Bayes algorithm is effective in classifying employee performance and can be utilized as a decision-support tool for determining the best quarterly employee. The implementation of this method is expected to enhance the objectivity, consistency, and transparency of employee performance evaluation at Statistics Indonesia (BPS) of Gorontalo Province.
Penguatan Literasi Digital Siswa SMA Melalui Pengenalan Computational Thinking, Coding, dan Artificial Intelligence di SMA Negeri 2 Gorontalo Utara Khairul Fathan Habie; Wahyudin Hasyim
Jurnal Karya untuk Masyarakat (JKuM) Vol 7 No 2: JULI 2026
Publisher : Universitas Tarakanita

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36914/2tphjq62

Abstract

Kemampuan literasi digital, khusunya computational thinking, koding, dan artificial intelligence (AI) saat ini menjadi salah satu kompetensi yang harus dimiliki oleh setiap orang di era revolusi industri 4.0 dan society 5.0 tidak terkecuali para siswa. Namun, pemanfaatan teknologi di kalangan siswa SMA masih dominan pada aspek konsumsi, belum pada pemahaman konseptual dan pemecahan masalah berbasis teknologi. Kegiatan pengabdian kepada masyarakat ini bertujuan untuk menguatkan literasi digital siswa SMA Negeri 2 Gorontalo Utara melalui pengenalan computational thinking, koding, dan artificial intelligence secara bertahap. Metode pelaksanaan yang digunakan adalah ceramah interaktif dan partisipatif dengan pendekatan bertahap yang terdiri dari perencanaan dan pelaksanaan. Tahap perencanaan, tim melakukan koordinasi dengan pihak sekolah, identifikasi kebutuhan siswa, serta penyusunan materi dan modul pelatihan. Tahap pelaksanaan dibagi ke dalam tiga sesi utama: pengenalan computational thinking melalui aktivitas unplugged activity, pengenalan koding dengan bahasa pemrograman Python dan platform CodeCombat, serta pengenalan kecerdasan buatan melalui contoh-contoh aplikasi AI dalam kehidupan sehari-hari. Kegiatan dilaksanakan di SMA Negeri 2 Gorontalo Utara, pada tanggal 07 April 2026 dengan durasi 2 Jam dan melibatkan siswa sebagai peserta. Keberhasilan kegiatan pelatihan diukur dengan rangkaian pre-test dan post-test. Hasil kegiatan menunjukkan adanya peningkatan pada indikator kesadaran dalam penggunaan AI, dari 7% sebelum pelatihan meningkat menjadi 32% setelah kegiatan.  
ANALISIS KEPUASAN PENGGUNA SISTEM INFORMASI MANAJEMEN KEPEGAWAIAN (SIMPEG) MENGGUNAKAN METODE END USER COMPUTING SATISFACTION (EUCS) DI PEMERINTAH KABUPATEN BOALEMO Rasmawati Ika; Frangky Tupamahu; Alter Lasarudin; Khairul Fathan Habie
Akademika : Jurnal Ilmiah Media Publikasi Ilmu Pengetahuan dan Teknologi Vol 15, No 2 (2026): Jurnal Akademika
Publisher : LPPM Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/akademika.v15i2.5830

Abstract

This study was conducted to map the level of user satisfaction with the Personnel Information System (SIMPEG) within the Boalemo Regency Government. A quantitative approach was applied, adopting the End User Computing Satisfaction (EUCS) framework. The research instrument, a questionnaire that had undergone validity and reliability testing, was distributed to 31 respondents, active users of the system. Data analysis revealed that, in aggregate, SIMPEG was deemed satisfactory, with an average score of 4.27, categorized as very satisfied. However, evaluation by dimension revealed disparities; the Content dimension recorded the highest score (4.42), while the Timeliness dimension ranked lowest with a score of 4.15 (categorized as satisfied). These findings indicate that although the information content is adequate, the speed of data access and updates remains a barrier. Therefore, the main recommendation focuses on improving system responsiveness to optimize personnel services
LAYANAN INFORMASI KEPEGAWAIAN BERBASIS CHATBOT WHATSAPP MENGGUNAKAN METODE RULE-BASED DI BKPSDM KABUPATEN BOALEMO Nunu Fadna Adam; Wahyudin Hasyim; Hilmansyah Gani; Khairul Fathan Habie
Akademika : Jurnal Ilmiah Media Publikasi Ilmu Pengetahuan dan Teknologi Vol 15, No 2 (2026): Jurnal Akademika
Publisher : LPPM Universitas Muhammadiyah Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31314/akademika.v15i2.5842

Abstract

This research aims to design and implement Whatsapp Chatbot-based Personnel Information Services at BKPSDM Boalemo Regency. Several obstacles in personnel information services include dependence on service officers, limited operating hours, and increasing need for information access. The development of the system uses the Waterfall method which includes needs analysis, design, implementation, testing, and maintenance. The chatbot is developed with rule-based techniques and integrated with the WhatsApp API. The results show that chatbots are able to provide real-time automated responses to personnel information, such as leave, promotion, periodic salary increases, mutations, and retirements. Black Box Testing shows that all system functions are running well and as needed. The results of the study show that the implementation of this chatbot increases information accessibility, reduces the workload of officers, and supports the digitization of public services at the Boalemo Regency BKPSDM
Effect of Learning Rate on VGG19 Model Architecture for Human Skin Disease Classification Khairul Fathan Habie; Murinto Murinto; Sunardi Sunardi; Arfiani Nur Khusna
Decode: Jurnal Pendidikan Teknologi Informasi Vol. 4 No. 3: NOVEMBER 2024
Publisher : Program Studi Pendidikan Teknologi Infromasi UMK

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51454/decode.v4i3.576

Abstract

The skin is the largest external organ that serves to protect human internal organs and is very sensitive to various diseases, so early detection is very important to reduce the risk and increase the chance of recovery. This study aims to classify skin disease types using CNN algorithm with VGG19 architecture and learning rate adjustment to get a more optimal model, using a dataset from Kaggle consisting of 3,295 images with six classes, including several types of skin diseases and one healthy skin class. The preprocessing process includes dividing the data into training and testing sets, resizing the images to fit the VGG19 architecture, and normalization to scale the pixel values from 0-255 to a range of 0-1. The results show that using a learning rate of 0.00003 produces the best performance with 97.29% accuracy, 97.36% precision, 97.29% recall, and 97.30% F1-score. These findings confirm that the CNN algorithm with VGG19 architecture can classify skin disease types well.
Co-Authors Alter Lasarudin Arfiani Nur Khusna Frangky Tupamahu Gani, Hilmansyah Hanna Fidri Mardiny Herman Yuliansyah, Herman Imam Riadi Murinto Murinto Murinto Murinto Nunu Fadna Adam Putro, Aldibangun Pidekso Rasmawati Ika Sunardi Sunardi Sunardi Sunardi Wahyudin Hasyim