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All Journal Jurnal Ilmu Komputer dan Informasi Bulletin of Electrical Engineering and Informatics Bulletin of Electrical Engineering and Informatics JAIS (Journal of Applied Intelligent System) EMITTER International Journal of Engineering Technology JURNAL NASIONAL TEKNIK ELEKTRO InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Bulletin of Electrical Engineering and Informatics Sistemasi: Jurnal Sistem Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING JMM (Jurnal Masyarakat Mandiri) Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) J-Dinamika: Jurnal Pengabdian Kepada Masyarakat Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Journal of Advanced in Information and Industrial Technology (JAIIT) Jurnal Pengabdian Masyarakat Indonesia Journal of Future Artificial Intelligence and Technologies Sasambo: Jurnal Abdimas (Journal of Community Service)
Farah Zakiyah Rahmanti
Telkom University
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Public Health Social Sciences Other

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Analyzing Quantum Feature Engineering and Balancing Strategies Effect on Liver Disease Classification Safriandono, Achmad Nuruddin; Setiadi, De Rosal Ignatius Moses; Dahlan, Akhmad; Rahmanti, Farah Zakiyah; Wibisono, Iwan Setiawan; Ojugo, Arnold Adimabua
Journal of Future Artificial Intelligence and Technologies Vol. 1 No. 1 (2024): June 2024
Publisher : Future Techno Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/faith.2024-12

Abstract

This research aims to improve the accuracy of liver disease classification using Quantum Feature Engineering (QFE) and the Synthetic Minority Over-sampling Tech-nique and Tomek Links (SMOTE-Tomek) data balancing technique. Four machine learning models were compared in this research, namely eXtreme Gradient Boosting (XGB), Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) on the Indian Liver Patient Dataset (ILPD) dataset. QFE is applied to capture correlations and complex patterns in the data, while SMOTE-Tomek is used to address data imbalances. The results showed that QFE significantly improved LR performance in terms of recall and specificity up to 99%, which is very important in medical diagnosis. The combination of QFE and SMOTE-Tomek gives the best results for the XGB method with an accuracy of 81%, recall of 90%, and f1-score of 83%. This study concludes that the use of QFE and data balancing techniques can improve liver disease classification performance in general.
PKM-Penerapan Desa Mandiri Energi Berkelanjutan Melalui Pengembangan Teknologi PLTHV Di Desa Duren Berbasis Peningkatan Soft Skill Muhammad Hasan Basri; Farah Zakiyah Rahmanti; Ilmirrizki Imaduddin
Sasambo: Jurnal Abdimas (Journal of Community Service) Vol. 4 No. 4: November 2022
Publisher : Lembaga Penelitian dan Pemberdayaan Masyarakat (LITPAM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36312/sasambo.v4i4.865

Abstract

Desa Duren Kecamatan Gading terletak di daerah pegunungan sebelah selatan Kabupaten Probolinggo merupakan salah satu Desa pegunungan yang belum mendapatkan pasokan listrik dari PLN. Fokus dan arah pengabdian adalah pengembangan desa mandiri energi diwilayah mitra, pengembangan teknologi Pembangkit Listrik Tenaga Hydro Vortex, peningkatan kualitas keilmuan, serta dapat menerapkan sistem manajemen Maintenance (Pemeliharaan). Kegiatan PKM ini meliputi beberapa mitra yang ikut berpartisipasi diantaranya Kepadal Desa Duren, pemilik pembabgkit, warga Desa Duren, dan Pemuda Desa Duren. Permasalahan yang dihadapi mitra saat ini, dua aspek diantaranya, Aspek pertama terkait peningkatan kapasitas teknologi Pembangkit Listrik Tenaga Hydro Vortex serta aspek kedua terkait peningkatan manajemen Maintenance (Pemeliharaan) Pembangkit Listrik. Metode yang dilakukan pada program PKM ini yaitu metode pelatihan, dan pendampingan. Dari hasil pengabdian kepada masyarakat yang dilakukan diantaranya pelatihan teknologi pembangkit listrik tenaga hidro Vortex (plthv), pendampingan peningkatan pengoperasian pembangkit listrik tenaga hidro Vortex, pembinaan, pengelolaan pembangkit listrik melalui kelompok peguyuban, dan pembinaan soft skill sistem pemeliharaan pembangkit listrik menuju desa mandiri energi. Pkm-Implementation of Sustainable Energy Independent Villages Through the Development of PLTHV Technology in Duren Village Based on Soft Skill Improvement Duren Village, Gading District, is located in a mountainous area to the south of Probolinggo Regency, which is one of the mountainous villages that has not yet received electricity supply from PLN. The focus and direction of service is the development of energy independent villages in partner areas, development of Hydro Vortex Power Plant technology, improving scientific quality, and being able to implement a Maintenance management system. This PKM activity includes several participating partners including the Duren Village Head, the generator owner, Duren Village residents, and Duren Village Youth. The problems that partners are currently facing are two aspects, the first aspect is related to increasing the capacity of Hydro Vortex Power Plant technology and the second aspect is related to improving the management of Power Plant Maintenance. The methods used in this PKM program are training methods, and mentoring. From the results of community service carried out, including training on Vortex hydro power plant technology (PLTHV), assistance in improving the operation of Vortex hydro power plants, coaching, managing power plants through community groups, and coaching soft skills for power plant maintenance systems towards energy independent villages.
Improving 3D Human Pose Orientation Recognition Through Weight-Voxel Features And 3D CNNs Riansyah, Moch. Iskandar; Putra, Oddy Virgantara; Rahmanti, Farah Zakiyah; Priyadi, Ardyono; Wulandari, Diah Puspito; Sardjono, Tri Arief; Yuniarno, Eko Mulyanto; Hery Purnomo, Mauridhi
EMITTER International Journal of Engineering Technology Vol 13 No 1 (2025)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v13i1.847

Abstract

Preprocessing is a widely used process in deep learning applications, and it has been applied in both 2D and 3D computer vision applications. In this research, we propose a preprocessing technique involving weighting to enhance classification performance, incorporated with a 3D CNN architecture. Unlike regular voxel preprocessing, which uses a zero-one (binary) approach, adding weighting incorporates stronger structural information into the voxels. This method is tested with 3D data represented in the form of voxels, followed by weighting preprocessing before entering the core 3D CNN architecture. We evaluate our approach using both public datasets, such as the KITTI dataset, and self-collected 3D human orientation data with four classes. Subsequently, we tested it with five 3D CNN architectures, including VGG16, ResNet50, ResNet50v2, DenseNet121, and VoxNet. Based on experiments conducted with this data, preprocessing with the 3D VGG16 architecture, among the five architectures tested, demonstrates an improvement in accuracy and a reduction in errors in 3D human orientation classification compared to using no preprocessing or other preprocessing methods on the 3D voxel data. The results show that the accuracy and loss in 3D object classification exhibit superior performance compared to specific preprocessing methods, such as binary processing within each voxel.
Identification of Grape Plant Diseases Based on the Leaves using Naïve Bayes Ramadhan, Muhammad Akbar; Nusyura, Fauzan; Rahmanti, Farah Zakiyah
Sistemasi: Jurnal Sistem Informasi Vol 13, No 5 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i5.3444

Abstract

One way to see the signs of disease in grapevines is a change in leaf color. Ordinary people detect diseases in grapevines only based on subjective vision. On this basis, we need a system that can help the layman to be able to detect diseases in grapevines based on the color of the leaves using the Naïve Bayes algorithm classification method. This algorithm uses simple calculations, so the process is carried out faster. In this study, testing was carried out using the Naive Bayes classification model with 800 training data and 160 validation data. The accuracy results obtained are 90% using the color historgram scenario on channel RGB interval 16 and GLCM with features of dissimilarity, correlation, homogeneity, contrast pixel spacing 5. 90% accuracy is also obtained in the color histogram scenario on channel HSV with interval 16 and GLCM with features of dissimilarity, correlation, homogeneity at pixel spacing of 5. Thus, it can be concluded that the Naive Bayes classification model can gain application in identifying diseases in grapevines through leaf color analysis.
Co-Authors Achmad Nuruddin Safriandono Adhitya Nugraha Adi, Prajanto Wahyu Ainin Fadlilah, Annisa Akhmad Dahlan Anas Ittaqullah Ardian Yusuf Wicaksono Ardyono Priyadi Arkav Juliandri Ataka, Ion Berliana Amelia Bernadus Anggo Seno AjiAji Daniel Happy Putra De Rosal Ignatius Moses Setiadi Diah Puspito Wulandari Dimas Bagus Saputro Edy Winarno Eko Mulyanto Yuniarno Fahri Firdausillah Helmy Widyantara Heru Agus Santoso Ignatia Indreswari Ilmirrizki Imaduddin Ion Ataka Iwan Setiawan Wibisono Khodijah Amiroh Mauridhi Hery Purnomo Moch. Iskandar Riansyah Mohammad Hamim Zajuli Al Faroby Muhammad Hasan Basri Muhammad Iqbal Maulana Muhammad Ma'sum Juniyanto Muhammad Rafi Irzam Novita Kurnia Ningrum Nurdin, Abdul Muhamin Nusyura, Fauzan Oddy Virgantara Putra Ojugo, Arnold Adimabua Oktavia Ayu Permata Pangestu Widodo, Pangestu Philip Tobianto Daely Prajanto Wahyu Adi Prajanto Wahyu Adi Ramadhan, Muhammad Akbar Rose, Clarenza Dixie Septian Enggar Sukmana Setyawan, Nathanael Ferdian Putra Tri Arief Sardjono Wincoko Putro, Fidi