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All Journal Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) MATHunesa: Jurnal Ilmiah Matematika Algoritme Jurnal Mahasiswa Teknik Informatika Jurnal Pepadun Jurnal Sistim Informasi dan Teknologi Jurnal Masyarakat Madani Indonesia Journal of Management and Innovation Entrepreneurship (JMIE) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Journal of Informatics and Communication Technology (JICT) IT-Edu: Jurnal Information Technology and Education Akademika Sinergi : Jurnal Ilmiah Multidisiplin
Hasanatul Iftitah
Universitas Jambi
Aerospace Engineering Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Library & Information Science Mathematics Physics Public Health Social Sciences Other

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Pemberdayaan Siswa SMK melalui Pelatihan IoT untuk Sistem Monitoring Tanaman dalam Mendukung Smart Farming Khaira, Ulfa; Saputra, Edi; Waladi, Akhiyar; Perdana, Yogi; Hanum, Nindy Raisa; Iftitah, Hasanatul; Ashar, Rahmad; Rozi, Syamsyida
Jurnal Masyarakat Madani Indonesia Vol. 5 No. 1 (2026): Februari
Publisher : Alesha Media Digital

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59025/9xvxt587

Abstract

Provinsi Jambi memiliki potensi besar di sektor pertanian, namun praktik konvensional masih menghadapi kendala terkait efisiensi, produktivitas, dan keberlanjutan. Smart farming berbasis Internet of Things (IoT) hadir sebagai solusi inovatif, namun implementasinya memerlukan sumber daya manusia yang kompeten. Kegiatan Pengabdian kepada Masyarakat (PPM) ini melibatkan sebanyak 20 orang siswa SMK, serta guru SMKN 1 Muaro Jambi, dengan tujuan meningkatkan kompetensi peserta dalam merakit, memprogram, dan memanfaatkan sistem monitoring tanaman berbasis IoT. Program dilaksanakan dalam empat sesi pelatihan yang meliputi pengenalan konsep smart farming dan IoT, pengenalan komponen, praktik perakitan sistem, serta dasar pemrograman mikrokontroler. Hasil evaluasi menunjukkan peningkatan signifikan pemahaman peserta, yang ditunjukkan oleh kenaikan nilai rata-rata post-test sebesar 26 poin (dari 37,1 menjadi 63,1), serta keberhasilan peserta dalam merakit prototipe sistem monitoring tanaman yang fungsional. Temuan ini menegaskan bahwa pelatihan berbasis praktik mampu menjembatani kesenjangan kompetensi siswa vokasi terhadap kebutuhan teknologi pertanian modern. Keberlanjutan program ini diharapkan dapat memperkuat peran sekolah vokasi dalam mendukung penerapan pertanian cerdas sekaligus membuka peluang karier dan kewirausahaan di bidang agroteknologi.
ANALISIS TEORI ANTRIAN PADA POM BENSIN NUSA INDAH JAMBI MENGGUNAKAN METODE SINGLE CHANNEL PHASE Amisha Dewi, Agnela; Ramadhani, Hana Oktavia; Ramadhani, Selvi Ayu; Khaira, Ulfa; Iftitah, Hasanatul
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 1 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/mathunesa.v14n1.p377-384

Abstract

Penelitian ini menganalisis fenomena antrian pada Pom Bensin Nusa Indah, Jambi, dengan menggunakan metode single channel phase. Data dikumpulkan melalui observasi langsung selama 3 jam dengan total 152 kendaraan. Parameter antrian dihitung menggunakan perangkat lunak POM QM dan rumus klasik M/M/1 untuk mendapatkan tingkat utilisasi (ρ), probabilitas sistem kosong (P0), rata-rata jumlah pelanggan dalam antrian (Lq), rata-rata jumlah pelanggan dalam sistem (L), waktu tunggu rata-rata dalam antrian (Wq) dan waktu rata-rata dalam sistem (W). Hasil menunjukkan tingkat utilisasi ρ = 0,50; P0 = 0,50; Lq ≈ 0,51; L ≈ 1,02; Wq ≈ 0,01 jam (≈0,6 menit); W ≈ 0,02 jam (≈1,2 menit). Temuan ini menunjukkan performa pelayanan yang relatif memadai, tetapi masih ada peluang perbaikan dalam pengaturan jalur layanan dan staffing pada jam sibuk. Implikasi manajerial dan rekomendasi operasional disajikan untuk meningkatkan efisiensi pelayanan dan kepuasan pelanggan
Analisis Teori Permainan Dalam Menentukan Strategi Persaingan Pada Platform E-Commerce Shopee Dan TikTok Shop Aliyah, Rihhadatul; Valerina Noa Verent; Lesianda Junitia; Ulfa Khaira; Hasanatul Iftitah
IT-Edu : Jurnal Information Technology and Education Vol. 11 No. 01 (2026): Volume 11 No. 01 (2026)
Publisher : Universitas Negeri Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26740/it-edu.v11i01.73647

Abstract

Abstrak: Persaingan pesat saat ini terutama pada platform e-commerce antara Shopee dan TikTok Shop semakin terus berkembang, hal ini dapat dianalisis lebih lanjut dengan menggunakan pendekatan Teori Permainan untuk dapat melihat kedua platform tersebut dalam menentukan strategi yang lebih berdaya saing. Analisis tersebut dilihat dari penilaian strategi berdasarkan produk, harga, kemudahan dalam akses, promosi dan pelayanan yang telah dikumpulkan dari 40 responden dengan penyebaran kuesioner. Perhitungan menggunakan strategi murni untuk mengetahui nilai Saddle Point sebagai indeks yang stabil dari strategi, lalu hasilnya diuji kembali menggunakan Software POM QM for Windows untuk memastikan kesamaan. Dari hasil yang telah ditemukan, Saddle Point berada pada nilai 16 yang artinya permainan tersebut mencapai strategi optimal, kondisi optimal berada pada X3 (Shopee) dan Y1 (TikTok Shop). Analisis ini telah memberikan aspek kuantitatif terkait persaingan antara kedua platform untuk menghadapi kompetisi yang lebih serius di masa depan. Kata Kunci: Teori Permainan, Strategi Murni, Persaingan;
Parameter-Efficient Models for Malaria Detection and Classification Using Small-Scale Imbalanced Blood Smear Images Akhiyar Waladi; Hasanatul Iftitah; Nindy Raisa Hanum; Yogi Perdana; Fitra Wahyuni; Rahmad Ashar
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 11, No. 2, May 2026
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v11i2.2558

Abstract

Malaria diagnostic automation faces critical challenges, including severe class imbalance with ratios of up to 54:1, limited datasets containing 200 to 500 images, and computational inefficiency resulting from the need to train separate models for each detection-classification combination. This study developed a multi-model framework with a shared classification architecture that trains classification models once on ground-truth crops and reuses them across all detectors. The framework systematically evaluated three YOLO Medium architectures for parasite detection and six CNN architectures for lifecycle and species classification across four complementary malaria datasets totaling 1,544 microscopy images. Detection achieved mAP@50 scores ranging from 70.84% to 96.27%, with high recall values of 71.05% to 93.12% minimizing missed parasite detections. Classification results demonstrated the importance of dataset-dependent model selection, with parameter-efficient EfficientNet models containing 5.3M to 9.2M parameters consistently outperforming ResNet variants with up to 44.5M parameters. EfficientNet-B1 achieved accuracies of 91.51% on the IML Lifecycle dataset and 98.28% on the MP-IDB Species dataset, while EfficientNet-B0 achieved 86.45% on the multi-patient MD-2019 dataset. ResNet50 achieved 96.13% accuracy on severely imbalanced MP-IDB Stages dataset. Focal Loss optimization with alpha = 1.0 and gamma = 1.5 enabled robust minority-class performance, achieving F1-scores between 0.44 and 1.00 on ultra-minority classes and demonstrating effective handling of class imbalance. The compact models, with sizes ranging from 46 MB to 89 MB, enable practical deployment on resource-constrained hardware.
Evaluating Kolmogorov-Arnold Networks for Multispectral Land Cover Classification Using Sentinel-2 Imagery in Jambi City Akhiyar Waladi; Hasanatul Iftitah
Jurnal Pepadun Vol. 7 No. 1 (2026): April
Publisher : Department of Computer Science, Faculty of Mathematics and Natural Sciences, University of Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/pepadun.v7i3.334

Abstract

Land cover maps obtained from satellite imagery are used in environmental management and spatial planning. Deep learning now outperforms traditional machine learning for this task, but Kolmogorov-Arnold Networks (KAN) have rarely been tested on multispectral remote sensing data. This paper evaluates two KAN strategies for classifying nine land cover types from Sentinel-2 imagery in Jambi, Indonesia. ResNet-KAN adds a KAN-based classifier head to a standard CNN backbone, while ConvKAN builds the entire network from KAN-based convolution layers. Both are compared against seven CNN, Transformer, and machine learning baselines using 23 spectral features with Google Dynamic World labels as reference, and ablation experiments test spectral feature composition, ImageNet transfer learning, and input patch size. Swin Transformer reaches the highest overall accuracy (88.34%), but ConvKAN better separates rare land cover classes like Grass and Shrub, achieving the best F1-Macro (0.5870) with only 2.91 million parameters, 89.4% fewer than Swin-T. Adding spectral indices raises ConvKAN F1-Macro by 13.8%, but lowers ViT accuracy by 3.19% OA because self-attention can already learn band-ratio operations from raw bands. KAN models also perform better when trained from scratch, because most Sentinel-2 channels fall outside the visible spectrum that ImageNet covers. Spatially, ConvKAN produces maps as clean as Swin Transformer despite being ten times smaller. KAN can therefore match larger models in accuracy and map quality for multispectral land cover classification.
Co-Authors Abda Abda Afifa, Afifa Lutfia Fakhira Akhiyar Waladi Akhiyar Waladi Akhiyar Waladi Aliyah, Rihhadatul Amisha Dewi, Agnela Anisa Bella Panjaitan Ashar, Rahmad Devi Listiani Safitri Edi Saputra Fitra Wahyuni Halimatus Sakdiah Daulay Khaira, Ulfa Lesianda Junitia Lilian Putri Salsabila M. Muzzainur Rahman Muhammad Rumii Firnanditya Nasution, Mukhtada Billah Nindy Raisa Hanum Perdana, Yogi Putri, Anastasya Alya Rahmad Ashar Rahmad Ashar, S.Kom. M.Kom. Raihan Naufal Muzakki Ramadhani, Hana Oktavia Ramadhani, Selvi Ayu Reza Safitri Riyan Ardani Sonya Diabel Melanie Munthe Syamsyida Rozi Ulfa Khaira, S.Komp., M.Kom. Ulfa Khaira, Ulfa Utomo, Pradita Eko Prasetyo Valerina Noa Verent Vira Aldina Pratiwi Yogi Perdana Yogi Perdana Yuanita Clark Lestari Simbolon Yuhandri Yuhandri