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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Tren Bisnis Global JPEG Jurnal Topik Global Jurnal Teknoinfo
M. Ramaddan Julianti
Institut Teknologi dan Bisnis Bina Sarana Global
Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences

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DECISION SUPPORT SYSTEM UNTUK PROPERTI PREMIUM: INTEGRASI AHP DAN TOPSIS DALAM MENGANALISIS PROPERTI SINAR MAS GROUP Aldo Putra Ramaddan; M. Ramaddan Julianti; Nunung Nurmaesah
Jurnal Teknoinfo Vol. 19 No. 2 (2025): July 2025 Period
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/teknoinfo.v19i2.372

Abstract

Implementation of decision support system (dss) to select the best premium property from sinar mas group in bsd city. the main problem is the complexity of decision making in the premium property segment involving various criteria such as price, location, facilities, land area, and investment potential. the aim of this study is to develop a dss that integrates the analytical hierarchy process (ahp) method to determine criteria weights and the technique for order preference by similarity to ideal solution (topsis) to rank property alternatives based on proximity to the ideal solution. the research methods include criteria identification, data collection, criteria weight calculation using ahp, normalization and preference value calculation using topsis, and result evaluation. the results show that the elyon property type is the best alternative with the highest preference value of 0.74112, followed by adora primes and terravia belova. this study demonstrates the effectiveness of integrating ahp and topsis in providing objective and measurable decision recommendations for premium property purchases. this system is expected to assist prospective buyers, developers, and property consultants in making better decisions and has the potential to be developed into a digital application.
DEEP LEARNING APPROACH FOR RECOGNIZING SUBSIDIZED GAS RECIPIENTS USING CONVOLUTIONAL NEURAL NETWORKS Achmad Sidik; M. Bucci Ryando; M. Ramaddan Julianti; Agus Rifaldi
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.7454

Abstract

Inaccurate targeting in subsidized LPG distribution remains a persistent policy challenge in Indonesia, where manual verification processes are vulnerable to misuse and administrative error. Addressing this gap, the present study develops and evaluates a biometric identity verification system based on Convolutional Neural Networks (CNNs) to improve the accuracy and accountability of subsidy allocation at the point of distribution. Following the CRISP-DM framework, two CNN architectures with fundamentally different design philosophies were compared: ResNet-IR, optimized for representational depth and recognition accuracy, and MobileFaceNet, designed for computational efficiency on resource-constrained hardware. Both models were sourced from the InsightFace framework as pre-trained models and evaluated on a locally acquired dataset of 111 registered subsidy recipients from Pajang Village, Tangerang City. Evaluation across face identification (1:N) and face verification (1:1) tasks reveals that ResNet-IR consistently outperforms MobileFaceNet, achieving an accuracy of 94.7% with a precision, recall, and F1-score of 0.9043, compared to MobileFaceNet’s accuracy of 93.7% and F1-score of 0.8862. The primary contribution of this work is to demonstrate, for the first time in the Indonesian subsidy distribution context, that deep learning-based facial recognition can serve as a viable, deployable mechanism for biometric identity verification in public service programs offering a technically grounded pathway toward more transparent and equitable subsidy targeting.
Co-Authors Achmad Sidik Agus Rifaldi Agus Rohman Aldo Putra Ramaddan Andika Sanjaya Andri Nur Rohman Ane Meliane Dedi Dedi Dhimas Ikhsan Wijaya Ertalita Br Sembiring Fajar Jafarullah Ahmad Ghazi Azhari Najih Heriyanto Heriyanto Junando Akas Suwandara M. Ali Akbar Jufri M. Bucci Ryando Mei Yofena Mila Amri Nurmaesah, Nunung Pramana Anwas Panchadria Reza Eka Angga Saputra Reza Nabili Akbar Ridwan Abdul Hakim Rizqi Diva Wardana Putra Rosana Junita Sirait Saipullah Saipullah Sutarman Sutarman Tri Astuti