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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Sistemasi: Jurnal Sistem Informasi AKSIOLOGIYA : Jurnal Pengabdian Kepada Masyarakat JITK (Jurnal Ilmu Pengetahuan dan Komputer) IJISTECH (International Journal Of Information System & Technology) Jurnal Mantik Journal of Computer System and Informatics (JoSYC) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) IJISTECH Journal of Applied Data Sciences KLIK: Kajian Ilmiah Informatika dan Komputer Jurnal Abdimas Bina Bangsa Jurnal Media Informatika Jurnal Sains Teknologi dan Sistem Informasi Prosiding Snastikom Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen)
Dedy Hartama
STIKOM Tunas Bangsa
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Social Sciences Other

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Optimizing Disaster Response: A Systematic Review of Time-Dependent Cumulative Vehicle Routing in Humanitarian Logistics Dedy Hartama; Wanayumini Wanayumini; Irfan Sudahri Damanik
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 10 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v10i3.29686

Abstract

Effective delivery of aid during disasters is crucial for mitigating impacts and ensuring well-being. A major challenge in humanitarian logistics is optimizing vehicle routing to maximize efficiency and minimize delivery times. which included 50 studies published between 2012 and 2022. We used the prism method to guide the process of choosing a study, which started from 200 Abstract which is identified and ends with 50 appropriate studies for in -depth analysis. This systematic literature review (SLR) examines the Time-Dependent Cumulative Vehicle Routing Problem (TDCVRP) in humanitarian logistics, identifying VRP variants, their applications, and effectiveness in disaster scenarios. Using a comprehensive search and PRISMA guidelines, the review highlights the importance of optimization models and advanced algorithms. Applications include aid delivery, evacuation management, and facility location optimization, though challenges like computational complexity and reliance on real-time data persist. The review identifies research gaps and suggests future research should focus on integrating advanced methods and improving practical applicability in disaster responses.
Prediksi Harga Rumah di Jakarta Menggunakan Pendekatan Hybrid K-Means Clustering dan Decision Tree kasih siregar; Andini Nurjannah; Citra Ayuningtyas; Bagas Try Atmaja; Dedy Hartama
Jurnal Media Informatika Vol. 7 No. 4 (2026): Edisi Juli - Agustus
Publisher : Lembaga Dongan Dosen

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Abstract

Harga rumah merupakan salah satu aspek penting dalam sektor properti yang dipengaruhi oleh berbagai karakteristik, seperti lokasi, luas tanah, luas bangunan, jumlah kamar, dan fasilitas pendukung. Penelitian ini bertujuan untuk menganalisis pengaruh penambahan informasi hasil clustering terhadap performa prediksi harga rumah di Jakarta menggunakan pendekatan Hybrid K-Means Clustering dan Decision Tree. Dataset yang digunakan adalah Jakarta House Price Dataset yang diperoleh dari Kaggle dengan jumlah 10.000 data rumah. Tahapan penelitian meliputi pembersihan data, penanganan outlier menggunakan metode Interquartile Range (IQR), transformasi data, pembentukan cluster menggunakan algoritma K-Means, serta pembangunan model prediksi menggunakan Decision Tree. Hasil penelitian menunjukkan bahwa model hybrid memperoleh nilai RMSE sebesar 2.097.559.711 dan R² sebesar 0,6044, yang lebih baik dibandingkan model Baseline Decision Tree. Sementara itu, proses clustering menghasilkan tiga cluster dengan nilai Silhouette Score sebesar 0,1436, yang menunjukkan bahwa kualitas pemisahan antar cluster masih tergolong rendah, namun tetap mampu memberikan informasi tambahan sebagai fitur pada model prediksi. Hasil analisis juga menunjukkan bahwa luas tanah dan luas bangunan merupakan faktor yang paling berpengaruh terhadap harga rumah. Dengan demikian, pendekatan Hybrid K-Means Clustering dan Decision Tree dapat digunakan sebagai alternatif dalam prediksi harga rumah, meskipun kualitas clustering masih perlu ditingkatkan pada penelitian selanjutnya.
MULTI-OBJECTIVE OPTIMIZATION OF RESNET50 ARCHITECTURE USING GENETIC ALGORITHM FOR ENHANCED BATIK MOTIF CLASSIFICATION P.A.M. ZIDANE R.W.P.P ZER ZER; Dedy Hartama; Agus Perdana Windarto
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 12 No. 1 (2026): JITK Issue August 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v12i1.8050

Abstract

Automatic batik motif classification remains challenging due to the high visual similarity among many motifs, making manual differentiation difficult. This study proposes a weighted-sum multi-objective Genetic Algorithm (GA) to optimize a pre-trained ResNet50 model for batik motif classification. The novelty of this study lies in optimizing the top-layer architecture and transfer-learning hyperparameters of ResNet50 by jointly considering classification accuracy, trainable parameter count, and inference latency. Unlike many previous GA-based CNN studies that mainly focus on accuracy or construct architectures from scratch, this study employs GA as a resource-efficient optimizer for practical deployment. The research used a quantitative experimental design with a secondary dataset of 3,550 batik images from five motif classes, namely Kawung, Megamendung, Parang, Sidomukti, and Truntum. The optimization process searched for the best configuration of four hyperparameters, namely the number of neurons in the dense layer, dropout rate, learning rate, and fine-tuning depth, while Adam was used as a fixed optimizer throughout the experiments. The results show that the proposed model improved classification accuracy from 84.00% to 90.00%, reduced trainable parameters by 94.4% from 23.59 million to 1.31 million, and decreased inference time by 29.4% compared with the baseline ResNet50 model. These findings indicate that the proposed method can achieve a favorable balance between predictive performance and computational efficiency for cultural heritage recognition on resource-constrained devices.
Co-Authors Abdi Rahim Damanik Abdullah Ahmad Agus Perdana Windarto Alan Boy Sandy Damanik Andini Nurjannah Asro Pradipta Bagas Try Atmaja Bahrudi Efendi Damanik Citra Ayuningtyas Dewi, Rafiqa Fahmi Firzada Febriani Fitri Anggraini Iin Parlina Ika Okta Kirana Indra Gunawan Irfan Sudahri Damanik Jalaluddin J Jalaluddin Jalaluddin kasih siregar Lili Tanti M. Safii MARIA BINTANG Mawaddah Anjelita Mochamad Wahyudi Muhammad Irfani Muhammad Rahmansyah Siregar Muhammad Ridwan Lubis Notryady Purba P.A.M. ZIDANE R.W.P.P ZER ZER Poningsih Poningsih Renaldi Rudi Handika Safii Safii Saifullah Saifullah Solikhun S Solikhun Solikhun Sumarno Sumarno Sumarno Sumarno Surya Darma Wanayumini Wanto, Anjar Wendi Robiansyah Widodo Saputra Yogi Prayoga Zulaini Masuro Nasution