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Fathoni -
Information Systems Universitas Sriwijaya Palembang, Indonesia
Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Neuroscience Public Health Social Sciences Other

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Penerapan Analisis Sentimen Berita Keuangan dari IDX Channel dan Long Short-Term Memory (LSTM) dalam Prediksi Perubahan Harga Saham pada Perusahaan XYZ Fathoni Fathoni; Cantika Aulia; Muhammad Fakhri Nadrota Acta; Eka Saputra; Muhammad Fadhil Rahman; Ali Ibrahim
JOISIE (Journal Of Information Systems And Informatics Engineering) Vol. 9 No. 2 (2025)
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/joisie.v9i2.5009

Abstract

Investasi saham semakin diminati, namun fluktuasi harga yang tinggi membuat prediksi menjadi tantangan. Harga saham dipengaruhi oleh banyak hal, seperti berita keuangan yang menunjukkan sentimen pasar. Namun, banyak model prediksi bergantung pada data sebelumnya tanpa memperhitungkan pendapat media. Penelitian ini bertujuan untuk memprediksi perubahan harga saham perusahaan XYZ dengan menggabungkan model Long Short-Term Memory (LSTM) dan analisis sentimen terhadap berita keuangan dari IDX Channel. Data historis harga saham diambil dari Yahoo Finance, sedangkan data sentimen diperoleh melalui klasifikasi opini berita berbasis model IndoBERT. Data kemudian diproses menggunakan pendekatan normalisasi Min-Max Scaling dan dibentuk dalam format time series menggunakan teknik window sliding dengan time step sebesar 30. Hasil penelitian menunjukkan bahwa model LSTM mampu memprediksi harga saham dengan nilai RMSE sebesar 35,92 dan korelasi positif lemah antara sentimen dan harga saham sebesar 0,27. Prediksi harga satu hari ke depan yang dihasilkan model menunjukkan nilai Rp 4.097,00. Visualisasi residual menunjukkan sebaran kesalahan yang stabil di sekitar nol, menunjukkan generalisasi model yang cukup baik.
Analisis dan Perbandingan Akurasi Image Generative AI DALL-E 3 dan Midjourney Menggunakan Metode Frechet Inception Distance (FID) Alif Baidhawi; Allan Nugraha; Fathoni Fathoni; Deni Agus Hendrawan; M Bintang Naufal Riansyah; Ali Ibrahim
JURNAL PETISI (Pendidikan Teknologi Informasi) Vol. 7 No. 1 (2026): JURNAL PETISI (Pendidikan Teknologi Informasi)
Publisher : Universitas Pendidikan Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36232/jurnalpetisi.v7i1.3172

Abstract

Perkembangan teknologi Generative AI telah membawa kemajuan pesat dalam pembuatan gambar berbasis teks, dengan DALL-E 3 dan Midjourney sebagai dua model terdepan. Penelitian ini bertujuan untuk menganalisis dan membandingkan akurasi visual hasil gambar dari kedua model menggunakan metrik Frechet Inception Distance (FID). Lima jenis prompt teks dipilih secara sistematis berdasarkan kategori anjing ras tertentu, dan setiap model menghasilkan 50 gambar yang dibandingkan dengan gambar acuan dari dataset Stanford Dogs. Hasil penelitian menunjukkan bahwa DALL-E 3 memiliki rata-rata skor FID sebesar 19.85, sedangkan Midjourney sebesar 28.42, yang berarti DALL-E 3 menghasilkan gambar yang lebih mendekati visual nyata. Uji statistik menggunakan Independent Sample t-Test menunjukkan adanya perbedaan signifikan antara kedua model. Dengan demikian, DALL-E 3 lebih cocok untuk kebutuhan yang menuntut realisme visual, sedangkan Midjourney lebih unggul dalam eksplorasi artistik.
Comparative Analysis of Environmental Permitting in Indonesia and Malaysia: Implications for National Strategic Projects Fathoni Fathoni; Febrian Febrian; Firman Muntaqo; Mohammad Nizar
Administrative and Environtmental Law Review Vol 6 No 1 (2025)
Publisher : Fakultas Hukum Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25041/aelr.v6i1.4360

Abstract

This manuscript presents a comparative analysis of environmental permitting frameworks in Indonesia and Malaysia, specifically focusing on their application to large-scale development initiatives, particularly National Strategic Projects (PSN) . As developing economies, both nations grapple with the imperative of balancing economic growth with environmental sustainability. This study employs a comparative juridical–normative approach, drawing on statutory frameworks, secondary literature, and policy documents from Indonesia and Malaysia. Additionally, this study examines the legal and procedural aspects of environmental impact assessment [Analisis Mengenai Dampak Lingkungan (AMDAL) in Indonesia and Environmental Impact Assessment (EIA) in Malaysia], highlighting key similarities and differences in their approaches. While both countries possess established legislative frameworks for environmental protection, Indonesia’s accelerated permitting for the PSN, commonly facilitated by the Online Single Submission system, has drawn criticism for potentially compromising environmental safeguards and exacerbating social and environmental conflicts. Conversely, Malaysia relies on its more standardized EIA process for major projects, although it faces challenges in ensuring effective enforcement and public participation. The analysis demonstrates that despite distinct regulatory evolutions and implementation strategies, both nations share common struggles in mitigating the adverse environmental and social consequences of rapid development. The findings underscore the urgent need to strengthen environmental governance, enhance transparency, and promote meaningful stakeholder engagement to foster genuinely sustainable development pathways in Indonesia and Malaysia.
Klasifikasi Status Gizi Bayi sebagai Upaya Deteksi Dini Masalah Gizi Menggunakan Algoritma Naive Bayes Muhammad Rafif AR; Fathoni Fathoni; Ken Ditha Tania; Allsela Meiriza; Ali Ibrahim
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.9490

Abstract

The classification of infant nutritional status remains a significant challenge in the utilization of health data, particularly in developing predictive models that are accurate and reliable for supporting decision-making at the primary healthcare level. This study aims to implement the Naïve Bayes algorithm to classify infant nutritional status based on anthropometric indicators, namely weight-for-age (W/A) and height-for-age (H/A). The data used in this study were obtained from Posyandu activity reports at Citra Medika Public Health Center covering the period from 2023 to 2025, with a total of 10,654 infant records. The class distribution in the dataset includes normal, well-nourished, undernourished, severely undernourished, overnourished, and at risk of overnutrition categories. Since the class proportions are relatively balanced, no oversampling technique was applied. The research process involved several stages, including data cleaning, category normalization, class label transformation, and dataset splitting using stratified sampling with a composition of 80% training data and 20% testing data. The evaluation results indicate that the Naïve Bayes model achieved an accuracy of 82%. The precision values were 0.92 for the normal class, 0.39 for the malnutrition class, and 0.14 for the overweight class. The recall values were 0.89 for the normal class, 0.63 for the malnutrition class, and 0.08 for the overweight class. Meanwhile, the F1-scores were 0.83 for the normal class, 0.48 for the malnutrition class, and 0.11 for the overweight class. These findings suggest that the model demonstrates fairly good performance in classifying infant nutritional status based on anthropometric data. Therefore, the Naïve Bayes algorithm can be considered effective for classifying infant nutritional conditions using W/A and H/A indicators. This study is expected to contribute to the development of data-driven systems to enhance analytical quality and support decision-making in primary healthcare services.
Analisis Disparitas Data Kebencanaan Indonesia sebagai Dasar Mitigasi Berbasis K-Means Vanisa Amalia Putri; Fathoni Fathoni; Yadi Utama
Building of Informatics, Technology and Science (BITS) Vol 8 No 2 (2026): September 2026
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v8i2.9667

Abstract

Indonesia experiences a high frequency of disasters; however, differences in characteristics between national and global datasets create challenges for consistent disaster analysis. Previous studies generally rely on a single data source, limiting the ability to capture disparities in data representation comprehensively. This study aims to analyze disparities in disaster data representation and identify regional disaster patterns using a data mining approach. A Disaster Severity Index was constructed using Min-Max normalization, and the K-Means algorithm was applied to cluster regions based on disaster index and event frequency. The optimal number of clusters was determined using the Elbow Method and validated using the Silhouette Score, while a global dataset was used for comparison. The results indicate that the optimal model is achieved at K=4 with a Silhouette Score of 0.610883, indicating good cluster separation. Most regions exhibit moderate disaster characteristics, while a small number show extreme patterns with disproportionate relationships between frequency and severity. Differences between national and global datasets suggest variations in reporting mechanisms and data coverage. These findings demonstrate that relying on a single data source may lead to biased interpretations, highlighting the importance of multi-source data integration to improve the accuracy of disaster analysis.
Co-Authors Abdiansah, Abdiansah Ahmad Fali Oklilas Ahmad Rafid Rizqullah Ahmad Rifai Al Farissi Ali Ibrahim Alif Baidhawi Alisia Silver Stone Allan Nugraha Allsela Meiriza Allsela Meiriza, Allsela Alzaini, Akbar Apriansyah Putra Apriansyah Putra Apriyadi Apriyadi Ari Wedhasmara Arvhi Randita Setia Cantika Aulia Dedy Kurniawan Deni Agus Hendrawan Dian Febriansyah Eka Saputra Endang Lestari Ruskan Ermatita - Erwin, Erwin Febrian Febrian Firman Muntaqo Juseia Wulandari Ken Ditha Tania Ken Ditha Tania Leiden Fauzi Yoka Surya Lyvia Valentina M Bintang Naufal Riansyah M Ihsan Jambak Maharani Maharani Mira Afrina Mohammad Nizar Muhammad Fadhil Rahman Muhammad Fakhri Nadrota Acta Muhammad Rafif AR Muhammad Rezky Naretha Kawadha Pasemah Gumay Pacu Putra Purwita Sari Putri Mutiara Arinie Rahmat Fadli Isnanto Rahmat Izwan Heroza Rahmat Maulana Rezqe, Beriadi Agung Nur Rio Abdi Sucipta Rizka Dhini Kurnia Sanjaya, M. Rudi Siti Nur Arafah Sultan Arba'i Suluh Arif Wibowo Syamsuryadi Syamsuryadi Tika Octri Dieni Vanisa Amalia Putri Violin Juneyla Nandita Yadi Utama Yadi Utama Yadi Utama Zikri Firmansyah