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All Journal Semantik Techno.Com: Jurnal Teknologi Informasi Prosiding SNATIF Jurnal Transformatika Scientific Journal of Informatics Seminar Nasional Informatika (SEMNASIF) Journal of Information System Sinkron : Jurnal dan Penelitian Teknik Informatika Journal of Information Technology and Computer Science Jurnal Pilar Nusa Mandiri Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control IKRA-ITH Informatika : Jurnal Komputer dan Informatika JOURNAL OF APPLIED INFORMATICS AND COMPUTING Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Sistem Komputer dan Informatika (JSON) Jurnal Pengabdian Literasi Digital Indonesia Indonesian Journal of Health Information Management Services (IJHIMS) Community: Jurnal Pengabdian Pada Masyarakat Prosiding Seminar Riset Mahasiswa
Fahmi Amiq
Fakultas Ilmu Komputer, Universitas Dian Nuswantoro, Semarang 50131
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Public Health Social Sciences Other

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Pendampingan Pembuatan Video Animasi untuk Siswa SMA At Thohiriyyah Semarang Astuti, Yani Parti; Utomo, Danang Wahyu; Sudibyo, Usman; Fahmi, Amiq; Kartikadarma, Etika; Dolphina, Erlin; Subhiyakto, Egia Rosi
Community : Jurnal Pengabdian Pada Masyarakat Vol. 4 No. 3 (2024): November : Jurnal Pengabdian Pada Masyarakat
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/3s4ejr82

Abstract

Information technology has developed and provided progress such as the increasing use of computers and the internet in the world, especially in the world of education. Through computers and the internet, all information can be disseminated and can be used as learning materials for students. The development of information technology and the internet, not all information is disseminated positively. Some information is disseminated negatively such as fake news (hoaxes), radicalism, and hate speech. There needs to be skills in using the development of information technology. Digital literacy trains users not only to be proficient in using information technology but also to have the ability to think critically, creatively, and innovatively to produce digital competence. SMA At Thohiriyyah is one of the high schools in Semarang that focuses on understanding and improving the abilities of its students in digital literacy. Insight is needed for SMA At Thohiriyyah students in understanding the importance of digital literacy. Animation video training is one way to increase student creativity in digital literacy in creating learning videos. With this training, it is hoped that students can develop learning videos that can be used on social media such as YouTube
Manajemen Sampah Dalam Meningkatkan Circular Economy Di Desa Kebuman, Kecamatan Banyubiru, Semarang Hadi, Heru Pramono; Gamayanto, Indra; Faisal, Edi; -, Suhariyanto; Fahmi, Amiq
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 7, No 1 (2024): JANUARI 2024
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v7i1.1743

Abstract

Abstrak Permasalahan sampah sudah menjadi permasalahan dunia, terutama sampah anorganik dan B3 yang tidak dapat diurai secara alami, sementara jumlah produksi sampah terus bertambah seiringdengan pertumbuhan penduduk. Dari data statistik Kabupaten Semarang jumlah sampah yang terangkut mulai tahun 2019 sebanyak : 220 487 M3, tahun 2020 : 247 095 M3 dan tahun 2021 : 280 859 M3, hal ini menunjukkan peningkatan jumlah sampah naik secara liner. Desa KebumenKecamatan Banyubiru Kabupaten Semarang menghadapi permasalahan yang serupa dengan meningkatnya volume sampah rumah tangga berdampak pada lingkungan yang kurang sehat. Meskipun sudah ada bank sampah pada wilayah tersebut namun ada beberapa kendala yangdihadapi yaitu manajemen sampah, reduce, reuse dan recycle atau 3 R belum optimal. Program PKM (Program Kemitraan Masyarakan) Universitas Dian Nuswantor dengan penerapan manajemen sampah yang efektif dan efisien dengan metode FDG (Focus Group Discussion) dan Edukasi dan Pelatihan diharapkan sampah yang terdapat diwilyah tersebut diolah baik sehingga dapat meniminalkan dampak negatif sampah terhadap lingkungan hidup desa Kebumen dan dapatmenciptakan circular ekonomi, sehingga dapat meningkatkan taraf ekonomi masyarakat setempat Kata kunci: Pengelolaan, Sampah, Manajemen, Taraf Hidup, Ekonomi
Klasifikasi Teks Twitter Menggunakan Algoritma Naïve Bayes untuk Analisis Sentimen Penggunaan Vaksin Covid-19 Rohim, Abdul; Fahmi, Amiq
Prosiding Seminar Riset Mahasiswa Vol 1, No 1: Maret 2023
Publisher : Universitas Islam Sultan Agung

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Pandemi Covid-19 berdampak buruk terutama pada sektor kesehatan, ekonomi, dan pendidikan. Pemerintah Indonesia melakukan pencegahan dengan melakukan vaksin dosis ke-1 dan ke-2. Namun, dinilai masih kurang efektif untuk menghambat penyebaran virus. Selanjutnya diperkuat dengan melakukan vaksin ke 3 (booster). Tujuan dari penelitian ini untuk menganalisis sentimen pada masyarakat mengenai pelaksanaan vaksin booster. Analisis ini untuk membantu stakeholder dalam memahami sentimen masyarakat baik positif, netral, maupun negatif. Data yang digunakan sebanyak 1.122 tweet dengan menggunakan kata kunci "vaksin booster dan covid". Pada penelitian ini, kami menggunakan algoritma Naïve Bayes untuk prediksi sentimen analisis. Dataset untuk pelatihan dan pengujian sebesar 90% (1.009) dan tes 10% (113). Hasil eksperimen menghasilkan akurasi prediksi sebesar 72%, precission 68%, recall 74%, F1-score 70%, dan nilai AUC/ROC 82%. Hasil analisis sentimen "netral" sebanyak 518 (46.2 %), "positif" sebanyak 437 (38.9%), dan "negatif" sebanyak 167 (14.9%). Hasil dapat diartikan bahwa Algoritma Naïve Bayes memiliki performa klasifikasi yang baik untuk target sentimen multi-kelas.Keyword: Analisis Sentimen, Text mining, Naïve Bayes, Vaksin Booster, Covid-19.
Implementation of DBSCAN Algorithm for Grouping Poverty Levels in Central Java Province Fahmi, Amiq; Tsani, Maulida Aristia
Jurnal Sistem Komputer dan Informatika (JSON) Vol. 6 No. 4 (2025): Juni 2025
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/json.v6i4.8553

Abstract

Poverty is a complex problem that hampers socio-economic development in Indonesia, especially in Central Java Province, which encounters significant challenges, with a poverty rate reaching 10.77% in 2023. This study aims to identify spatial patterns of poverty in 35 districts/cities in Central Java Province by grouping areas based on the number of poor individuals reported by the Central Java Province Statistics Agency (BPS) in 2023. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm groups districts/cities based on poverty data density with optimized parameters to produce statistically significant clusters. The results of the analysis reveal four clusters, specifically cluster 0 (moderate poverty), cluster 1 (high poverty), cluster 2 (very high poverty), and cluster 3 (low poverty). Model validation was executed using the Silhouette Score (0.447) and Davies-Bouldin Index (0.441), which showed the validity of the clustering. This study is anticipated to provide strategic implications for the Central Java Provincial Government in formulating more effective poverty alleviation policies, such as resource allocation adjusted to each cluster's characteristics. In addition, this study enables future exploration of additional socio-economic factors influencing poverty, such as the Human Development Index, education, health, infrastructure, resource accessibility, and comparative analysis of clustering algorithms for enhanced accuracy.
Proboboost: A Hybrid Model for Sentiment Analysis of Kitabisa Reviews Prasetya, Rakan Shafy; Fahmi, Amiq; Sulistyono, MY Teguh
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11138

Abstract

The rapid advancement of digital technology has significantly transformed public behavior in social activities, particularly in online donations and zakat payments. The Kitabisa application was selected in this study not only for its popularity but also due to its high user engagement and large volume of reviews on the Google Play Store, making it an ideal representation of public trust in Indonesia’s digital philanthropy ecosystem. This research aims to analyze user sentiment toward the Kitabisa application using a hybrid Proboboost model, which combines Multinomial Naive Bayes (MNB) and Gradient Boosting Classifier through a soft voting mechanism. The model is designed to address class imbalance and improve accuracy in short-text sentiment analysis for the Indonesian language. The study employed preprocessing techniques including case folding, text cleaning, stopword removal, and stemming using the Sastrawi algorithm. Feature extraction was performed using TF-IDF, with an 80:20 train-test split and 5-fold cross-validation to ensure model reliability. Experimental results indicate that the Proboboost model achieved an accuracy of 89.51% and an F1-score of 87.4%, outperforming the Naive Bayes baseline with 87.98% accuracy. The sentiment distribution demonstrates a dominance of positive sentiment (87.24%), followed by negative (8.53%) and neutral (4.23%) reviews. These findings suggest that users generally express satisfaction and trust toward the Kitabisa platform. The results also confirm that the hybrid Proboboost model effectively balances classification performance between majority and minority sentiment classes, offering deeper insights into user perceptions of digital philanthropic services.
Optimized LSTM with TSCV for Forecasting Indonesian Bank Stocks Salsabila, Rizka Mars; Fahmi, Amiq; Al Zami, Farrikh
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.11314

Abstract

Volatility in financial markets presents complex forecasting challenges for investors, particularly within emerging economies such as Indonesia. This study proposes an optimized Long Short-Term Memory (LSTM) model for forecasting the stock prices of five significant Indonesian banks: BBCA, BBRI, BMRI, BBNI, and BBTN, utilizing daily OHLCV data (Open, High, Low, Close, Volume) and technical indicators from 2020 to 2025. The dataset comprises over 6,000 daily records, segmented using a sliding window approach to preserve temporal structure and enhance learning efficiency. Concurrently, the model architecture comprising dual LSTM layers with dropout regularization was refined through systematic hyperparameter tuning to enhance predictive performance. Model evaluation employed 5-fold Time Series Cross-Validation (TSCV), a sequential validation technique that mitigates data leakage and explicitly overcomes the limitations of conventional k-fold methods by preserving chronological integrity. Performance metrics included MSE, RMSE, MAE, R², and MAPE. The experiment results demonstrate the model’s robustness in capturing long-term dependencies within financial time series. BBCA and BMRI achieved superior accuracy (R² > 0.95), with BBCA recording the lowest MAPE of 2.34%. Despite market fluctuations, the model maintained consistent reliability across all test folds. This study overcomes a methodological limitation by integrating LSTM with TSCV in expanding markets, offering actionable insights for investors, analysts, and policymakers, and serving as a reference for adaptive AI-based, more informed forecasting tools. Moreover, the proposed framework holds promise for broader application across other financial sectors and regional markets with similar volatility characteristics.
PENGUATAN KAPASITAS BIDAN DAN KADER PUSKESMAS DUREN: TRAINING OF TRAINER MODEL PENDAMPINGAN DIGITAL PERAWATAN KEHAMILAN-NIFAS Wulandari, Respati; Wibowo, Syifa Sofia; Apriyanti, Apriyanti; Fahmi, Amiq; Laurensius Tokan, Geraldinho; Yumna Huwaida, Imtiyaz
Indonesian Journal of Health Information Management Services Vol. 5 No. 2 (2025): Indonesian Journal of Health Information Management Services (IJHIMS)
Publisher : APTIRMIKI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33560/ijhims.v5i2.148

Abstract

The phenomenon of teenage pregnancy has serious health, social, and economic impacts. Globally, 55% of pregnancies among 15-19 year olds result in unsafe abortions, including in Indonesia. Based on interviews with the midwife coordinator, BOK funds are only sufficient to hold classes for 15 pregnant women. There is a disparity between the number of pregnant women and the amount of assistance provided so far. Of the 399 pregnant women, only 18.8% (75 women) can be assisted through classes for pregnant women. The next problem is the low reading interest of mothers in KIA books. With the availability of digital educational media, it is hoped that this can be a solution to the problems occurring at the Duren Posyandu. The purpose of this activity is to increase the knowledge of midwife cadres regarding digital education models that can accommodate more pregnant women by using educational materials that are more interesting to pregnant women. The community service activity was carried out on July 31 - August 1, 2025, with 5 village midwives and 13 cadres participating. The activity began with a pre-test, followed by the community service team delivering the material, and finally a post-test. All participants in the activity experienced an increase in knowledge for 20 points, as evidenced by their post-test scores, which were higher than their pre test scores.
Evaluasi Komparatif Algoritma Naïve Bayes, KNN, Logistic Regression, SVM, dan Extra Trees untuk Analisis Sentimen Tokopedia Ciputra, Indramawan; Fahmi, Amiq
Building of Informatics, Technology and Science (BITS) Vol 7 No 3 (2025): December 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i3.8537

Abstract

The rapid evolution of digital technology has catalyzed a shift in consumer behavior, particularly in online shopping activities facilitated by e-commerce platforms such as Tokopedia. User-generated reviews yield large-scale textual data that can be systematically analyzed to uncover consumer sentiment in a factual and structured manner. This study aims to evaluate and compare the performance of five sentiment classification algorithms Naive Bayes, K-Nearest Neighbors (KNN), Logistic Regression, Support Vector Machine (SVM), and Extra Trees Classifier based on user review data from Tokopedia. The analytical workflow begins with web crawling, followed by text preprocessing procedures including tokenization, case folding, and stop-word removal, culminating in sentiment classification using the aforementioned algorithms. Performance evaluation was conducted using four standard metrics accuracy, precision, recall, and F1-score. The results reveal that SVM achieved the highest accuracy at 85%, outperforming KNN and Extra Trees Classifier (84%), Logistic Regression (82%), and Naive Bayes (79%). SVM’s superior performance is attributed to its ability to identify optimal hyperplanes that effectively separate sentiment classes, particularly in high-dimensional feature spaces. These findings offer practical insights for developers of sentiment analysis systems in selecting the most effective algorithm, while reinforcing the strategic application of Natural Language Processing (NLP) techniques within Indonesia’s e-commerce landscape.
Co-Authors -, Suhariyanto Abdul Rohim, Abdul Abu Salam Agus Winarno Agus Winarno, Agus Al zami, Farrikh Alzami, Farrikh Apriyanti Apriyanti Ardianda Aryo Prakoso Ariel Bagus Nugroho Asih Rohmani, Asih Astuti, Yani Parti Budi Harjo Budiono Budiono Candra Irawan Catur Supriyanto Ciputra, Indramawan Diana Purwitasari Edi Faisal Edi Sugiarto Edi Sugiarto Edi Sugiarto Edi Sugiarto Edi Sugiarto Edi Sugiarto Egia Rosi Subhiyakto, Egia Rosi Erlin Dolphina Etika Kartikadarma Fhaldian, Wahyu Fikri Budiman Hadi, Heru Pramono Harun Al Azies Hindarto, Aris Nur Husna, Farida Amila Indra Gamayanto ISWAHYUDI ISWAHYUDI Karis Widyatmoko Kurnia Desita, Raafi Lalang Erawan Laurensius Tokan, Geraldinho Lintang Mekar Tanjung Maurensa, Giacinta Mauridhi Hery Purnomo Megantara, Rama Aria Moch. Eko Rustiyono Muhammad Naufal, Muhammad Mulyanto, Edy Muslih Muslih MY. Teguh Sulistyono Nova Rijati Novi Hendriyanto, Novi Prasetya, Rakan Shafy Pujiono Pujiono Pujiono Pujiono Pujiono Ramadhan Rakhmat Sani Respati Wulandari Ridha Rahmawati Ridho Pambudi Rizky Adrianto Salsabila, Rizka Mars Sasono Wibowo Sidharta, Bayu Adjie Sihombing, Drigo Alexander Sri Winarno Sudibyo, Usman Suharnawi Suharnawi Suryo Adi Nugroho Tacharri, Chusnuut Tsani, Maulida Aristia Utomo, Danang Wahyu Wibowo, Syifa Sofia Yumna Huwaida, Imtiyaz Yuventius Tyas Catur Pramudi Zaenal Arifin Zahro, Azzula Cerliana