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All Journal TEKNIK Jurnal Pseudocode Journal of Information Systems Engineering and Business Intelligence Jurnal Obsesi: Jurnal Pendidikan Anak Usia Dini IT JOURNAL RESEARCH AND DEVELOPMENT Abdimasku : Jurnal Pengabdian Masyarakat JITU : Journal Informatic Technology And Communication TIN: TERAPAN INFORMATIKA NUSANTARA Journal Evaluation in Education (JEE) Pandawa : Pusat Publikasi Hasil Pengabdian Masyarakat Transformasi Masyarakat : Jurnal Inovasi Sosial dan Pengabdian Harmoni Sosial : Jurnal Pengabdian Dan Solidaritas Masyarakat Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Manfaat: Jurnal Pengabdian Pada Masyarakat Indonesia Ngudi Waluyo Empowerment : Jurnal Pengabdian Kepada Masyarakat
Alifia Revan Prananda
Department Of Information Technology, Faculty Of Engineering, Universitas Tidar
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Public Health Social Sciences Veterinary Other

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Using Support Vector Machines for Predicting and Mitigating Stunting in Early Childhood Education in Rural Semarang Novichasari, Suamanda Ika; Prananda, Alifia Revan; Suwidagdho, Dhanang; Fauziah, Syifa; Setya Wijaya, Vania Amelia; Adam, Otmar Shah
Jurnal Obsesi : Jurnal Pendidikan Anak Usia Dini Vol. 8 No. 5 (2024)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/obsesi.v8i5.6131

Abstract

In 2030, 70% of Indonesia's population will be of productive age (15-64 years), which is a demographic bonus. However, this potential is threatened by the high rate of stunting in children, which threatens future workforce productivity. Early identification of stunting risk is essential for timely intervention. This study develops a stunting prediction model using machine learning with data from early childhood education institutions in rural Semarang. The model used is a Support Vector Machine (SVM) implemented through the RapidMiner framework. The SVM model achieved an accuracy of 97.56%, a precision of 98.97%, a recall of 97.37%, and an AUC of 0.997. The results of the SVM model highlight the importance of physical motor skills and artistic development.
Penerapan Faster RCNN + ResNet 50 untuk Mengidentifikasi Spesies dan Stadium Parasit Plasmodium Malaria Prananda, Alifia Revan; Novichasari, Suamanda Ika; Fatkhurrozi, Bagus; Abdillah, Muhammad Nurkholis; Frannita, Eka Legya; Majidah, Zharifa Nur; Wibowo, Fadhila Syahida
TIN: Terapan Informatika Nusantara Vol 6 No 2 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i2.7187

Abstract

Malaria is one of the epidemic health diseases and is well-known as a serious infectious disease. The malaria examination process had occurred by analyzing the digital microscopic images using a microscope. Those examination procedures were conducted manually, which lead to some hurdles such as misinterpretation, misdiagnosis and may produce subjective results. This research aims to develop a method for detecting the Plasmodium parasite and identifying the species and stage of Plasmodium parasite. The proposed method was performed into 488 raw data comprising of 538 parasites. The proposed method was started by conducting a data augmentation process for balancing the number of data, training model, testing model, evaluation. In this study, both the training and testing processes were performed by applying Faster RCNN + ResNet-50. The result of the testing process shows that Faster RCNN + ResNet-50 successfully achieved mAP of 0,603. It also achieved accuracy of 93.91%, sensitivity of 66.20%, specificity of 96.10%, PPV of 60.14% and NPV of 97.30%. This result indicates that the proposed method is powerful for detecting Plasmodium parasites and identifying all species and stadiums.
Pengembangan Intelligent Leather Inspection Method Berbasis Interpretable Artificial Intelligence Frannita, Eka Legya; Wulandari, Dwi; Putri, Naimah; Rahmawati, Atiqa; Prananda, Alifia Revan
TIN: Terapan Informatika Nusantara Vol 6 No 2 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v6i2.7425

Abstract

The Industry 4.0 revolution, characterized by the widespread adoption of artificial intelligence and automation, has fundamentally transformed quality inspection processes in manufacturing sectors. Nevertheless, the leather tanning industry continues to rely on conventional visual inspection methods conducted by human operators, which are inherently susceptible to subjectivity, inter-operator variability, and inconsistent outcomes. This study proposes an integrated deep learning framework utilizing the NasNet-Large architecture combined with Local Interpretable Model-Agnostic Explanations (LIME) to automate objective defect detection and quality classification of pickled leather. The research employs a digital image dataset comprising four distinct leather grade categories, each annotated with expert-validated ground truth labels and professional interpretations. Experimental results demonstrate consistent model performance with 75% accuracy in both training and validation phases while achieving improved testing accuracy of 79%. LIME-based interpretability analysis reveals significant spatial convergence between model-identified defect regions and expert-annotated ground truth references. These findings indicate that the developed model exhibits remarkable competence in replicating professional leather quality inspection capabilities. The proposed approach not only enhances inspection efficiency by reducing human-dependent errors but also provides transparent decision-making interpretability - a critical requirement for reliable AI implementation in industrial applications. This research contributes to the advancement of explainable AI systems in material quality assessment, offering methodological innovation and practical implementation value for the leather manufacturing sector.
AR-FootIN 4.0 : Aplikasi Pengenalan Teknologi Industri 4.0 Pada Bidang Alas Kaki Berbasis Mobile Augmented Reality Prananda, Alifia Revan; Marwanto, Marwanto; Frannita, Eka Legya; Hidayat, Anwar
TIN: Terapan Informatika Nusantara Vol 4 No 10 (2024): March 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v4i10.4956

Abstract

Rapid development of technology gave a positive impact on the footwear industry. The emergence of various types of technology as part of the industrial revolution 4.0 has greatly helped various types of work in industry. However, technology also need to be supported by good quality resource. Knowledge regarding how to use and maintain these technologies is needed so that the benefits of these technologies can be utilized. An alternative way is by developing good quality of human resource to being proficient in using technology. Furthermore, cultivating technological literacy is also one of the essential factors. Regarding to this situation, we proposed research that aims to develop the AR-FootIN 4.0 application as a learning media for introducing industry 4.0 in the footwear sector. This learning media is developed by employing mobile augmented reality. The proposed learning media is developed by using the SDLC method. The resulted learning media is then evaluated by conducting two types of evaluation, which are expert evaluation and user evaluation. The results of expert evaluation and user evaluation obtain a percentage of 93.33% and 86% respectively, which means that the feasibility of the application to support the technological literacy process in the footwear industry is very good.
Penerapan Metode CNN (Convolutional Neural Network) untuk Mengklasifikasikan Jenis Cacat pada Kulit Hewan Frannita, Eka Legya; Prananda, Alifia Revan
TIN: Terapan Informatika Nusantara Vol 5 No 2 (2024): July 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/tin.v5i2.5390

Abstract

Recently, leather industry was rapidly growth in several countries. In Indonesia, leather industry became one of the government's priority industries since there were quite a lot of leather industries developing in various regions in Indonesia. On the other hand, there were large number of consumer demand for leather products. Regarding to this fact, maintaining the quality of leather was strongly important. An alternative solution for maintaining leather quality is to conduct leather quality inspection process. However, currently the leather inspection process was still carried out manually by identifying directly the types of defects found on the surface of the leather. This manual inspection process certainly has several hurdles such as time consuming, requiring high accuracy, and requiring experienced operators. This research aimed to develop convolutional neural network architecture that can classify types of leather defects. This research was done by conducting four main processes which were literature study and data collection processes, develop CNN architecture, training process, and testing process. This research work used public dataset consisting of 3600 digital leather images distributed into six classes (folding mask, grain off, growth marks, loose grains, pinhole, non-defective). Based on the training and testing process, the model obtained training accuracy of 90.43% and testing accuracy of 88.47%.
Pemberdayaan Keamanan Dan Kesejahteraan Melalui Penggunaan CCTV, Digital Marketing, dan Website Nawawi, Ibrahim; Wibowo, Rheza Ari; Salim, D Jayus Nor; Novichasari, Suamanda Ika; Nata, Imam Adi; Wicaksono, Damar; Prananda, Alifia Revan; Rakhmawati, Restu
Ngudi Waluyo Empowerment: Jurnal Pengabdian Kepada Masyarakat Vol. 2 No. 2 (2023): Ngudi Waluyo Empowerment: Jurnal Pengabdian Kepada Masyarakat
Publisher : Fakultas Komputer dan Pendidikan Universitas Ngudi Waluyo

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Desa Sidogede, yang terletak di Kecamatan Grabag, Kabupaten Magelang, memiliki potensi besar untuk meningkatkan keamanan dan kesejahteraan warganya. Proyek "Bakti Sosial Pemberdayaan Keamanan dan Kesejahteraan" bertujuan untuk mengintegrasikan teknologi CCTV, Digital Marketing, dan Website Desa Interaktif sebagai solusi holistik untuk meningkatkan kondisi sosial dan ekonomi di desa ini. Penggunaan CCTV akan memperkuat keamanan dengan memonitor aktivitas publik dan rumah-rumah warga. Digital Marketing akan membantu mendukung pengembangan usaha lokal dan mempromosikan produk-produk desa. Sementara itu, Website Desa Interaktif akan menjadi sumber informasi penting dan sarana partisipasi warga dalam pengambilan keputusan. Proyek ini tidak hanya bertujuan untuk meningkatkan tingkat keamanan fisik, tetapi juga ekonomi desa melalui promosi produk lokal dan peningkatan akses ke informasi. Dengan demikian, proyek ini diharapkan akan berdampak positif terhadap kesejahteraan dan kehidupan masyarakat Desa Sidogede, menjadikannya sebagai contoh terbaik bagi upaya pemberdayaan desa-desa di wilayah sekitarnya.
Using Support Vector Machines for Predicting and Mitigating Stunting in Early Childhood Education in Rural Semarang Suamanda Ika Novichasari; Alifia Revan Prananda; Dhanang Suwidagdho; Syifa Fauziah; Vania Amelia Setya Wijaya; Otmar Shah Adam
Jurnal Obsesi : Jurnal Pendidikan Anak Usia Dini Vol. 8 No. 5 (2024)
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/obsesi.v8i5.6131

Abstract

In 2030, 70% of Indonesia's population will be of productive age (15-64 years), which is a demographic bonus. However, this potential is threatened by the high rate of stunting in children, which threatens future workforce productivity. Early identification of stunting risk is essential for timely intervention. This study develops a stunting prediction model using machine learning with data from early childhood education institutions in rural Semarang. The model used is a Support Vector Machine (SVM) implemented through the RapidMiner framework. The SVM model achieved an accuracy of 97.56%, a precision of 98.97%, a recall of 97.37%, and an AUC of 0.997. The results of the SVM model highlight the importance of physical motor skills and artistic development.
Kontrol Pembatasan Konten Anak dan Penggunaan Platform Digital UMKM di Lingkungan Ibu Rumah Tangga Banyubiru Wicaksono, Damar; Fathony, Ikhwan Alfath Nurul; Prananda, Alifia Revan; Wibowo, Rheza Ari; Widyadhana, Sunny Alodia; Siddiq, Naufal Miftakhul
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 9, No 1 (2026): JANUARI 2026
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v9i1.3156

Abstract

Program pengabdian masyarakat di Desa Banyubiru ini bertujuan untuk meningkatkan literasi digital ibu rumah tangga dalam dua aspek utama, yaitu pengendalian konten digital bagi anak-anak dan pemanfaatan platform digital untuk mendukung usaha mikro kecil dan menengah (UMKM). Kegiatan dilatarbelakangi oleh rendahnya pemahaman masyarakat terhadap keamanan konten digital, privasi data pribadi, serta potensi ekonomi dari media digital. Metode yang diterapkan meliputi sosialisasi, pelatihan teknis, simulasi studi kasus, dan pendampingan langsung. Peserta dilatih untuk menggunakan fitur parental control, manajemen waktu layar (screen time management), serta praktik keamanan digital dalam penggunaan media sosial dan aplikasi UMKM. Selain itu, dilakukan edukasi mengenai strategi pemasaran digital dan pengelolaan akun usaha secara aman. Pendekatan partisipatif diterapkan dengan melibatkan ibu rumah tangga sebagai peserta aktif dalam diskusi, praktik, dan pembentukan kelompok belajar digital di tingkat desa. Evaluasi dilakukan melalui survei dan wawancara yang menunjukkan peningkatan signifikan dalam pemahaman peserta terhadap keamanan digital dan penggunaan platform UMKM, di mana lebih dari 80% peserta mampu menerapkan praktik pembatasan konten anak dan pengelolaan akun digital secara mandiri. Hasil kegiatan tidak hanya meningkatkan kesadaran terhadap pentingnya kontrol konten digital dalam keluarga, tetapi juga mendorong pemberdayaan ekonomi rumah tangga melalui literasi digital yang aman dan produktif. 
Development and Validation of the Vark Instrument in Citizenship Education Learning: A Structural Equation Modeling Approach for More Accurate Learning Style Measurement Delfiyan Widiyanto; Novitasari Novitasari; Mashud Syahroni; Alifia Revan Prananda; Tholibah Mujtahidah; Annisa Istiqomah
Journal Evaluation in Education (JEE) Vol 6 No 2 (2025): April
Publisher : Cahaya Ilmu Cendekia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37251/jee.v6i2.1561

Abstract

Purpose of the study: This research aims to develop and test the VARK learning style instrument in Citizenship Education Learning using SEM (Structural Equation Modeling) to produce a valid, reliable and goodness of fit instrument for more accurate measurement of learning styles. Methodology: This study employs quantitative methods to evaluate the VARK learning styles theory in citizenship education through three primary phases: (1) assessing interrater validity to verify the construct's feasibility, (2) establishing empirical validity via Confirmatory Factor Analysis (CFA) to examine the construct's effectiveness and reliability, and (3) utilizing Structural Equation Modeling (SEM) along with goodness-of-fit (GOF) analysis to determine the model's overall suitability. Main Findings: The findings of this research indicate that the VARK learning styles framework and related instruments in citizenship education are very suitable for measurement. The model meets the main criteria: (1) Inter-rater Validity Test confirms its validity, (2) Construct Validity Test using Confirmatory Factor Analysis shows validity and reliability, and (3) Goodness-of-Fit Test places the model in the acceptable fit category. So this instrument can be used to measure learning styles accurately. Novelty/Originality of this study: The novelty of this research lies in the development and instrument of VARK learning styles in Citizenship Education learning which was tested using Structural Equation Modeling (SEM) to produce a feasible and accurate instrument for measuring learning styles.
Co-Authors Abdillah, Mokhammad Nurkholis Adam, Otmar Shah Anisa Lestari Annisa Istiqomah Bagus Fatkhurrozi Cornelius Rangga Surya Kusuma D Jayus Nor Salim Damar Wicaksono Daniel Gunawan Delfiyan Widiyanto Dhanang Suwidagdho Dinda Kusumaningrum Dwi Wulandari Eka Legya Frannita Galih Slamet Hidayat, Anwar Ibrahim Nawawi Ikhwan Alfath Nurul Fathony Imam Adi Nata Irfandy Thalib Lidia Faridah Majidah, Zharifa Nur Marwanto Marwanto, Marwanto Mashud Syahroni Mohammad Norman Aulia Mufasir Nabila Fairuzzahra Niken Orisa Putri Novitasari Novitasari Otmar Shah Adam Pramitaningrum, Erlita Purwaningsih , Nunik Putri, Naimah Rahmawati, Atiqa Rakhmawati, Restu Rhema Chandrawati Sapto Nisworo Setiawan , Wawan Budi Setya Wijaya, Vania Amelia Siddiq, Naufal Miftakhul Suamanda Ika Novichasari Syifa Fauziah Syifa Fauziah Terbit Bagaskara Ahmad Terra Rhebekka Tholibah Mujtahidah Vania Amelia Setya Wijaya Wibowo, Fadhila Syahida Wibowo, Rheza Ari Widyadhana, Sunny Alodia