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All Journal Tematik : Jurnal Teknologi Informasi Komunikasi Journal of Artificial Intelligence and Engineering Applications (JAIEA) Jurnal Sistem Informasi dan Teknologi (SINTEK) AMMA : Jurnal Pengabdian Masyarakat
Fatihanursari Dikananda
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Humanities Automotive Engineering Chemistry Computer Science & IT Control & Systems Engineering Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Nursing

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Strategi Otomatisasi Pemasaran Digital UMKM Melalui Pelatihan AI Dalam E-Commerce Fatihanursari Dikananda; Nining Rahaningsih; Ridho Nugroho; Vicky Pamungkas
AMMA : Jurnal Pengabdian Masyarakat Vol. 3 No. 3 : April (2024): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

The utilization of Artificial Intelligence (AI) offers significant potential for Micro, Small, and Medium Enterprises (MSMEs) to optimize their digital marketing strategies on e-commerce platforms. This Community Partnership Program is designed to provide training to MSMEs regarding the application of AI in various aspects of digital marketing. The training covers customer experience personalization, market data analysis, promotion content optimization, and improved online advertising efficiency. Through this activity, it is expected that MSMEs can enhance their understanding and skills in implementing AI-based solutions to expand market reach, improve customer interaction, and achieve more optimal digital marketing results in the e-commerce era.
PKM: Kewirausahaan Digital Untuk Karang Taruna Desa Kedawung Fatihanursari Dikananda; Irfan Ali; Nizar Fazari Hidayat; Rheznandya Fahreza
AMMA : Jurnal Pengabdian Masyarakat Vol. 2 No. 3 (2023): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

The The rapid development of digital technology has opened new opportunities in the entrepreneurial field, especially among the youth. This study, titled “Digital Entrepreneurship for the Karang Taruna of Desa Kedawung,” is designed to identify existing digital potentials and to develop an innovative digital entrepreneurship model aimed at empowering Karang Taruna members by generating job opportunities and enhancing the welfare of the local community. The study employs a mixed-method approach by integrating both qualitative and quantitative methods to analyze the barriers, potentials, and optimal strategies in harnessing digital technology as an economic empowerment tool. The research methodology includes field surveys, in-depth interviews with business practitioners and community leaders, and a comprehensive literature review from various relevant sources. The findings indicate that the digital divide and insufficient training are the primary obstacles to effective digital entrepreneurship implementation. In response, an intensive mentoring program combined with digital literacy training emerges as an effective solution to overcome these challenges. This program not only improves technical skills related to digital platforms but also enhances managerial competencies and entrepreneurial creativity. Furthermore, strategic partnerships with industry players and local academics are expected to foster a sustainable business ecosystem. The implications of this research are significant, as it provides a replicable model for digital entrepreneurship development that can be adapted to similar rural contexts. Active involvement from both governmental and private sectors is crucial to support the program’s sustainability through funding, infrastructure development, and market access. Consequently, this study contributes not only to local economic development but also to reducing youth unemployment, raising community welfare, and accelerating digital transformation in rural areas.
Pelatihan Desain Grafis Menggunakan Canva Untuk Promosi Produk Lokal Irfan Ali; Fatihanursari Dikananda; Ridho Nugraha; Sri Ayuningsih
AMMA : Jurnal Pengabdian Masyarakat Vol. 2 No. 3 (2023): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

The advancement of digital technology provides significant opportunities for micro, small, and medium enterprises (MSMEs) to enhance the competitiveness of local products through engaging visual promotions. This study aims to evaluate the effectiveness of graphic design training using the Canva platform to support the promotion of local products by rural communities. The training program was conducted as part of a community service initiative focusing on improving digital literacy and design skills, especially among housewives and youth from local organizations. The program utilized a participatory approach with stages including observation, training, mentoring, and evaluation. Initial observations indicated that most participants lacked graphic design skills but showed high enthusiasm for learning. The training materials covered the basics of visual design, the use of graphic elements, and hands-on practice in creating posters, product catalogs, and social media content using Canva. Evaluation results showed significant improvement in both technical abilities and creative output among participants. Many were able to produce promotional content suitable for online publication. These findings suggest that accessible online design platforms such as Canva can effectively enhance community capacity in local product promotion. This initiative contributes to the empowerment of the village creative economy and strengthens local product identity through professional and appealing visualization. For sustainability, collaboration with related institutions and the development of local design communities are highly recommended.
Pelatihan Coding Dasar untuk Siswa Sekolah Menengah sebagai Upaya Penguatan Kompetensi Digital Fathurrohman; Fatihanursari Dikananda; Alimun Hakim; Anita Ayu Hardani
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 03 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

This Community Partnership Program aims to provide basic coding training to high school students to enhance their competence in the digital era. The training is designed to introduce programming concepts, computational logic, and problem-solving skills through an easy-to-understand programming language. Activities include an introduction to algorithms, simple data structures, and the development of small coding projects. It is expected that through this training, students can develop computational thinking skills, increase creativity, and prepare themselves to face technological challenges in the future.
Optimalisasi Penggunaan Aplikasi Digital Payment bagi Pedagang Pasar Tradisional Fatihanursari Dikananda; Dodi Solihudin; Anjar Ayuning Lestari; Athhar Hafizha Luthfi
AMMA : Jurnal Pengabdian Masyarakat Vol. 1 No. 04 (2022): AMMA : Jurnal Pengabdian Masyarakat
Publisher : CV. Multi Kreasi Media

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Abstract

This Community Partnership Program aims to optimize the use of digital payment applications among traditional market traders. Activities include education on the benefits of digital payment, training on application usage, and assistance in non-cash transactions. This program is expected to increase transaction efficiency, security, and financial accessibility for traders, while also promoting financial inclusion in traditional markets.
Penerapan Algoritma K-Means Clustering Untuk Mengelompokan Siswa SMK Al-Ma’rifah Berdasarkan Kehadiran Dila Nurhafidilah; Nana Suarna; Agus Bahtiar; Umi Hayati; Fatihanursari Dikananda
Jurnal Sistem Informasi dan Teknologi Vol 6 No 1 (2026): Jurnal Sistem Informasi dan Teknologi (SINTEK)
Publisher : LPPM STMIK KUWERA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56995/sintek.v6i1.212

Abstract

Penelitian ini bertujuan untuk mengelompokkan siswa SMK Al-Ma’rifah berdasarkan pola kehadiran menggunakan algoritma K-Means Clustering. Data yang dianalisis merupakan catatan kehadiran siswa tahun ajaran 2023/2024 yang meliputi jumlah hadir, izin, sakit, alfa, dan persentase kehadiran. Tahapan pra-pemrosesan data dilakukan melalui pembersihan dan normalisasi sebelum proses clustering. Penentuan jumlah klaster optimal menggunakan Elbow Method dan Silhouette Coefficient menunjukkan bahwa tiga klaster merupakan struktur terbaik. Hasil pengelompokan menghasilkan tiga kategori siswa, yaitu sangat disiplin, cukup disiplin, dan kurang disiplin. Evaluasi kualitas klaster menggunakan Silhouette Score dan Davies–Bouldin Index menunjukkan pemisahan klaster yang baik. Penelitian ini membuktikan bahwa K-Means Clustering efektif dalam mengidentifikasi pola kehadiran siswa dan  dapat mendukung pengambilan keputusan sekolah berbasis data dalam meningkatkan kedisiplinan siswa.
Analisis Sentimen Ulasan by.U dengan Pelabelan Rating dan Leksikon Menggunakan Multinomial Naïve Bayes Fatihanursari Dikananda; Bani Nurhakim; Dian Ade Kurnia; Ahmad Rifai; Mugi Praseptiawan
TEMATIK Vol. 13 No. 1 (2026): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2026
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v13i1.2996

Abstract

Perkembangan layanan telekomunikasi digital mendorong bertambahnya jumlah ulasan pengguna yang digunakan sebagai bahan informasi guna mendukung pengambilan keputusan berbasis data. Penelitian ini bertujuan menganalisis sentimen ulasan aplikasi by.U menggunakan dua metode pelabelan data, yaitu rating-based labeling dan lexicon-based labeling, menggunakan algoritma Multinomial Naïve Bayes (MNB). Metode penelitian menerapkan framework Knowledge Discovery in Databases yang meliputi tahapan selection, preprocessing, transformation, data mining, dan evaluation. Dataset penelitian diperoleh dari Google Play sebanyak 8.000 ulasan berbahasa Indonesia. Tahap prapemrosesan mencakup cleaning, case folding, normalisasi, tokenisasi, stopword removal, serta stemming. Representasi fitur dilakukan menggunakan TF-IDF, sedangkan penyeimbangan data diterapkan melalui metode SMOTE. Evaluasi model dilakukan menggunakan metrik accuracy, precision, recall, dan F1-score dengan skema 10-fold cross validation. Hasil penelitian menunjukkan bahwa pendekatan lexicon-based labeling memberikan performa yang lebih baik dibandingkan rating-based labeling. Pendekatan rating-based menghasilkan accuracy sebesar 82,59%, precision 83,79%, recall 82,59%, dan F1-score 82,43%. Sementara itu, pendekatan lexicon-based memperoleh accuracy sebesar 88,96%, precision 89,69%, recall 88,96%, serta F1-score 88,91%. Temuan tersebut menunjukkan bahwa strategi pelabelan memiliki pengaruh terhadap performa klasifikasi sentimen. Pendekatan berbasis leksikon dinilai lebih efektif karena mampu memahami konteks linguistik dan ekspresi emosional pengguna secara lebih baik dibandingkan pendekatan berbasis rating.
Penerapan Model LSTM Univariat dengan Walk-Forward Validation untuk Estimasi Harga Saham Nokia Ahmad Rifai; Roni Saputra; Dian Ade Kurnia; Fatihanursari Dikananda
TEMATIK Vol. 13 No. 1 (2026): Tematik : Jurnal Teknologi Informasi Komunikasi (e-Journal) - Juni 2026
Publisher : LPPM POLITEKNIK LP3I BANDUNG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.38204/tematik.v13i1.3000

Abstract

Prediksi harga saham merupakan permasalahan yang kompleks karena karakteristik data deret waktu finansial yang bersifat non-linear, volatil, dan dinamis. Meskipun algoritma Long Short-Term Memory (LSTM) terbukti efektif dalam menangkap pola temporal, banyak penelitian sebelumnya menggunakan pendekatan multivariat yang melibatkan variabel dengan korelasi sangat tinggi sehingga berpotensi menimbulkan redundansi informasi dan meningkatkan kompleksitas model. Penelitian ini mengusulkan model LSTM univariat untuk memprediksi harga saham Nokia Corporation (NOK) dengan menggunakan harga penutupan sebagai variabel masukan tunggal. Data historis harian periode 1 Oktober 2015 hingga 24 Oktober 2025 sebanyak 2.532 observasi diperoleh dari Yahoo Finance. Sebelum proses pemodelan, dilakukan analisis korelasi terhadap variabel Open, High, Low, Close, dan Volume. Hasil analisis menunjukkan bahwa variabel harga memiliki korelasi yang sangat tinggi (r > 0,99), sedangkan variabel Volume memiliki korelasi yang sangat rendah terhadap variabel harga (−0,052 ≤ r ≤ −0,043). Berdasarkan hasil tersebut, harga penutupan dipilih sebagai fitur utama dalam pemodelan. Untuk mengevaluasi performa model pada kondisi prediksi yang realistis, diterapkan metode Walk-Forward Validation (WFV) sebanyak 30 iterasi. Hasil pengujian menunjukkan bahwa model memperoleh nilai MSE sebesar 0,0260, RMSE sebesar 0,1613, MAE sebesar 0,1086, MAPE sebesar 2,75%, dan koefisien determinasi (R²) sebesar 0,9446. Hasil tersebut menunjukkan bahwa model mampu menjelaskan 94,46% variasi harga saham dengan tingkat kesalahan prediksi yang rendah. Penelitian ini menyimpulkan bahwa model LSTM univariat yang didukung oleh proses seleksi fitur yang sistematis dan validasi temporal yang robust mampu menghasilkan prediksi harga saham yang andal dengan kompleksitas yang lebih rendah dibandingkan pendekatan multivariat konvensional.
Analysis and Visualization of Sales Transaction Patterns using Decision Tree and Tableau Public Miftahul Akbar; Nining Rahaningsih; Irfan Ali; Fatihanursari Dikananda; Umi Hayati
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1849

Abstract

This study aims to analyze sales transaction patterns of rubber waste at PT Mandiri Enviro Technosio by integrating the Decision Tree algorithm with interactive visualization using Tableau Public. The dataset consists of 405 sales transactions recorded during the 2024–2025 period, comprising attributes such as transaction date, product type, quantity, unit price, total value, delivery region, and buyer category. The research methodology includes data acquisition, preprocessing to ensure data quality and consistency, construction of a classification model using the CART algorithm, evaluation of model performance through a confusion matrix, and development of interactive dashboards for enhanced interpretability. The Decision Tree model achieved an accuracy of 88.24% in classifying transaction values into low, medium, and high categories. Unit price and transaction period were identified as the most influential attributes in determining transaction value. Visualization using Tableau Public effectively presented the distribution of transaction values, sales trends, and geographical patterns, thereby strengthening analytical insights and supporting data-driven decision making. The integration of classification techniques and interactive visualization contributes to improving business intelligence capabilities and enables the formulation of more adaptive, evidence-based sales strategies.
Predicting Student Academic Performance Based on Learning Habits Using XGBoost and SHAP Siti Latifah; Martanto; Raditya Danar Dana; Fatihanursari Dikananda; Umi Hayati
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1860

Abstract

This study developed a model for predicting student academic achievement based on learning habits using the XGBoost algorithm and SHAP interpretability techniques. The secondary dataset contains 1,000 entries and 16 variables (for example, hours of study per day, mental health, frequency of exercise, social media use, hours of sleep) pre-processed including cleaning, imputation, encoding, and normalization before being divided into train–test (80:20) and validated using 5-fold CV. Three models were tested: Linear Regression, Random Forest, and XGBoost. Evaluation using RMSE, MAE, and R² showed that XGBoost achieved RMSE = 0.335, MAE = 0.266, and R² = 0.882, while Linear Regression showed the best performance according to R² in certain configurations (R² = 0.888; RMSE = 0.326). SHAP analysis revealed that the most influential features were hours of study per day, mental health scores, exercise frequency, duration of social media use, and hours spent watching Netflix. The findings confirm that students' study habits and psychological conditions are the main determinants of academic achievement variation; the use of interpretable features strengthens the readability of the model for education stakeholders. Research recommendations include testing the model on longitudinal datasets, integrating socioeconomic factors, and implementing data privacy procedures before institutional-scale implementation.
Co-Authors Ahmad Rifai Alimun Hakim Anggun Riani Anita Ayu Hardani Anjar Ayuning Lestari Athhar Hafizha Luthfi Bani Nurhakim Cep Lukman Rohmat Dian Ade Kurnia Dila Nurhafidilah Dodi Solihudin Edi Wahyudin Fathurrohman Irfan Ali Martanto . Miftahul Akbar Nana Suarna Nining Rahaningsih Nizar Fazari Hidayat Odi Nurdiawan Praseptiawan, Mugi Raditya Danar Dana Rheznandya Fahreza Ridho Nugraha Ridho Nugroho Roni Saputra Rudi Kurniawan Sifaul Qulub Siti Latifah Sri Ayuningsih Umi Hayati Vicky Pamungkas