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All Journal Jurnal Informatika dan Teknik Elektro Terapan Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Jurnal Riset Informatika Jurnal Riset Sistem Informasi dan Teknologi Informasi (JURSISTEKNI) KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika MATHunesa: Jurnal Ilmiah Matematika Algoritme Jurnal Mahasiswa Teknik Informatika Jurnal Pepadun Jurnal Sistim Informasi dan Teknologi 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 Social Sciences Other

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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.
TRANSFER LEARNING ARCHITECTURE SELECTION FOR REMOTE SENSING SCENE CLASSIFICATION Akhiyar Waladi; Hasanatul Iftitah
Jurnal Riset Informatika Vol. 8 No. 3 (2026): Juni 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i3.515

Abstract

Selecting a deep learning architecture for classifying remote sensing scenes usually involves comparing published accuracy across papers that each use different training protocols, making it unclear whether accuracy gaps reflect architecture or training differences. We isolate the architecture variable by evaluating eight models from three design families, five classical CNNs (ResNet-50, ResNet-101, DenseNet-121, EfficientNet-B0, EfficientNet-B3), two vision transformers (ViT-B/16, Swin Transformer), and one modernized CNN (ConvNeXt-Tiny), under identical training conditions on EuroSAT (10 classes, 27,000 Sentinel-2 patches) and UC Merced (21 classes, 2,100 aerial photographs). Every model shares the same ImageNet-1K initialization, AdamW optimizer, augmentation pipeline, and early stopping rule. ConvNeXt-Tiny reached the highest accuracy on EuroSAT (99.11%) and Swin-T on UC Merced (99.76%), but the accuracy range on EuroSAT was only 0.41 percentage points (1.66 on UC Merced). McNemar's test confirmed that most pairwise differences were not significant. EfficientNet-B0, the smallest model at 4.0M parameters, reached 98.76% and 99.52% while using 21x fewer parameters than ViT-B/16. On these two well-studied benchmarks, a single uniform training configuration was sufficient to bring all architectures to near-identical performance. This convergence, observed under one fixed protocol and a single data partition, suggests that on saturated classification tasks the choice of architecture may be secondary to the choice of training procedure. Whether this convergence holds on harder benchmarks, under architecture-specific optimal configurations, or with domain-specific pretraining remains to be tested
Evaluasi Performa Algoritma Machine Learning untuk Klasifikasi Sentimen Ulasan Film IMDb Akhiyar Waladi; Hasanatul Iftitah
KERNEL: Jurnal Riset Inovasi Bidang Informatika dan Pendidikan Informatika Vol 7, No 1 (2026)
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/j.kernel.2026.v7i1.8567

Abstract

IMDb with over 83 million active users generates millions of movie reviews annually. Manually analyzing such large textual data is impractical, necessitating automated approaches for binary sentiment classification. This study tested three traditional machine learning algorithms, namely Logistic Regression, Naive Bayes, and Random Forest, on 50,000 English-language movie reviews with balanced distribution between positive and negative sentiments. Feature extraction was performed using CountVectorizer and TF-IDF after preprocessing steps including HTML tag removal, contraction expansion, and text normalization. Among all combinations tested, Logistic Regression with TF-IDF achieved the best results with 87.29% accuracy, 87.32% F1-score, and 0.9481 AUC-ROC. Performance consistency was confirmed through 5-fold cross-validation with low coefficient of variation across all folds. Feature importance analysis revealed that words like "excellent," "perfect," and "amazing" indicate positive sentiment, while "worst," "awful," and "terrible" mark negative sentiment. Naive Bayes excelled in speed with 11.5 seconds training time but lower accuracy, while Logistic Regression offered the best balance between accuracy and efficiency at 17.2 seconds. Beyond accuracy, Logistic Regression provides interpretable coefficients explaining which words drive predictions. The findings demonstrate that properly tuned traditional algorithms remain viable for sentiment analysis in the entertainment industry
ANALISIS TEORI ANTRIAN PADA POM BENSIN NUSA INDAH JAMBI MENGGUNAKAN METODE SINGLE CHANNEL PHASE Agnela Amisha Dewi; Hana Oktavia Ramadhani; Selvi Ayu Ramadhani; Ulfa Khaira; Hasanatul Iftitah
MATHunesa: Jurnal Ilmiah Matematika Vol. 14 No. 01 (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 SENTIMEN TERHADAP APLIKASI DUOLINGO MENGGUNAKAN INDOBERTWEET Muhammad Farhan Hadrawi; Rizqa Raaiqa Bintana; Hasanatul Iftitah
Jurnal Informatika dan Teknik Elektro Terapan Vol. 14 No. 3 (2026)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v14i3.10147

Abstract

Ulasan pengguna pada Google Play Store memuat informasi penting mengenai pengalaman, kepuasan, dan keluhan terhadap aplikasi pembelajaran bahasa seperti Duolingo. Namun, jumlah ulasan yang besar dan tidak terstruktur menyebabkan analisis secara manual menjadi kurang efisien. Penelitian ini bertujuan untuk mengklasifikasikan sentimen ulasan pengguna aplikasi Duolingo berbahasa Indonesia menggunakan model IndoBERTweet serta mengevaluasi kinerja model tersebut. Data awal terdiri atas 1.000 ulasan yang dikumpulkan melalui teknik web scraping dari Google Play Store. Setelah proses pelabelan dan validasi data, diperoleh 998 ulasan yang diklasifikasikan ke dalam sentimen positif, negatif, dan netral. Tahapan penelitian meliputi pengumpulan dan penyaringan data, pelabelan oleh tiga anotator, preprocessing teks, pembagian data dengan rasio 70:10:20, penyeimbangan data latih menggunakan RandomOverSampler, fine-tuning IndoBERTweet, dan evaluasi model. Hasil penelitian menunjukkan bahwa model memperoleh accuracy sebesar 0,9050, F1-score macro sebesar 0,7301, dan F1-score weighted sebesar 0,9155. Model menghasilkan performa terbaik pada sentimen positif, sedangkan sentimen netral menjadi kelas yang paling sulit dikenali. Hasil tersebut menunjukkan bahwa IndoBERTweet mampu mengklasifikasikan sentimen ulasan pengguna Duolingo dengan performa yang baik, meskipun masih dipengaruhi oleh ketidakseimbangan distribusi kelas.
Co-Authors Abda Abda Afifa, Afifa Lutfia Fakhira Agnela Amisha Dewi Akhiyar Waladi Aliyah, Rihhadatul Anisa Bella Panjaitan Devi Listiani Safitri Fitra Wahyuni Halimatus Sakdiah Daulay Hana Oktavia Ramadhani Khaira, Ulfa Lesianda Junitia Lilian Putri Salsabila M. Muzzainur Rahman Muhammad Farhan Hadrawi Muhammad Rumii Firnanditya Nasution, Mukhtada Billah Nindy Raisa Hanum Putri, Anastasya Alya Rahmad Ashar Rahmad Ashar, S.Kom. M.Kom. Raihan Naufal Muzakki Reza Safitri Riyan Ardani Rizqa Raaiqa Bintana Selvi Ayu Ramadhani Sonya Diabel Melanie Munthe Ulfa Khaira, S.Komp., M.Kom. Utomo, Pradita Eko Prasetyo Valerina Noa Verent Vira Aldina Pratiwi Yogi Perdana Yogi Perdana Yuanita Clark Lestari Simbolon Yuhandri Yuhandri, Yuhandri