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All Journal Jurnal Informatika dan Teknik Elektro Terapan JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Teknologi Informasi dan Komunikasi Jurnal Ekonomi Journal of Artificial Intelligence and Engineering Applications (JAIEA) BULLET : Jurnal Multidisiplin Ilmu AMMA : Jurnal Pengabdian Masyarakat JUKEMAS : Jurnal Pengabdian Kepada Masyarakat
Dikananda, Fatihanursari
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Segmentasi Minat Mahasiswa Terhadap Program Studi Menggunakan Algoritma K-Means Clustering Wahyudin, Edi; Dikananda, Fatihanursari
BULLET : Jurnal Multidisiplin Ilmu Vol. 2 No. 1 (2023): BULLET : Jurnal Multidisiplin Ilmu
Publisher : CV. Multi Kreasi Media

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Abstract

Segmenting student interest in study programs is a crucial step in strategic decision-making within higher education. By identifying interest groups, institutions can design more relevant curricula and develop more effective marketing strategies. This study aims to cluster student interests in study programs using the K-Means Clustering algorithm. The data used in this research were obtained from questionnaires assessing the interests and preferences of new students towards various study programs. The results of applying the K-Means algorithm indicate that students can be grouped into several clusters based on the similarity of their interests, which can be utilized to support academic policy and program promotion strategies.
PKM: Kewirausahaan Digital Untuk Karang Taruna Desa Kedawung Dikananda, Fatihanursari; Ali, Irfan; Fazari Hidayat, Nizar; Fahreza, Rheznandya
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 Ali, Irfan; Dikananda, Fatihanursari; Nugraha, Ridho; Ayuningsih, Sri
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.
ANALISIS SENTIMEN PADA ULASAN ACCESS BY KERETA API INDONESIA DENGAN K-NEAREST NEIGHBOR Rhamadanti, Anisa; Rifa'i, Ahmad; Dikananda, Fatihanursari; Anam, Khaerul
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 1 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i1.3961

Abstract

Abstrak. Access by KAI adalah suatu aplikasi pembelian tiket kereta api indonesiaKAI Access diluncurkan pada 4 September 2014, aplikasi ini mengalami pembaruan pada 10 Agustus 2023 dan diubah namanya menjadi Access by KAI, menjadi versi ke-5 dari PT. Kereta Api Indonesia (PERSERO). Metodologi penelitian melibatkan studi literatur, pengumpulan data melalui teknik web scraping, dan analisis terhadap 3715 ulasan pengguna dari 10 Agustus hingga 10 November 2023. Data tersebut mencatat 555 ulasan positif dan 3160 ulasan negatif, dengan penilaian 1 dan 2 dianggap sebagai negatif, sementara 3, 4, dan 5 dianggap positif. Setelah proses preprocessing, algoritma K-NN diimplementasikan untuk analisis sentimen. Dengan parameter optimal K=17 pada rasio pembagian data uji 90:10, model mencapai tingkat akurasi tinggi dengan presisi 87%, recall 100%, dan f1-score 93%. Pada rasio 80:20 dengan K=9, presisi meningkat menjadi 88%, recall mencapai 99%, namun f1-score tetap 93%. Pada rasio 70:30 dengan K=9, model menunjukkan presisi 87%, recall 99%, dan f1-score 93. Temuan ini diharapkan memberikan wawasan berharga bagi pengembang aplikasi untuk meningkatkan kualitas dan kepuasan pengguna pada platform "Access by KAI." Keywords: analisis sentimen, K-Nearest Neighbors (K-NN), ulasan pengguna, Access by KAI, web scraping 
Analysis and Visualization of Sales Transaction Patterns using Decision Tree and Tableau Public Akbar, Miftahul; Rahaningsih, Nining; Ali, Irfan; Dikananda, Fatihanursari; Hayati, Umi
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 Latifah, Siti; Martanto; Dana, Raditya Danar; Dikananda, Fatihanursari; Hayati, Umi
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.
PERBANDINGAN KINERJA SUPPORT VECTOR MACHINE ORIGINAL DAN SUPPORT VECTOR MACHINE BERBASIS SMOTE UNTUK ANALISIS SENTIMEN APLIKASI JKN MOBILE APIPAH, KUSNATUL; Martanto, Martanto; Dikananda, Fatihanursari; Bahtiar, Agus; Narasati, Riri
JURNAL TEKNOLOGI INFORMASI DAN KOMUNIKASI Vol. 17 No. 1 (2026): Maret
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtikp.v17i1.1403

Abstract

Analisis sentimen terhadap ulasan pengguna aplikasi JKN Mobile digunakan untuk mengevaluasi kualitas layanan digital BPJS Kesehatan. Penelitian ini membandingkan kinerja algoritma Support Vector Machine (SVM) pada data asli dan SVM berbasis Synthetic Minority Oversampling Technique (SMOTE) dalam mengatasi permasalahan ketidakseimbangan kelas. Dataset diperoleh melalui proses scraping ulasan pengguna aplikasi JKN Mobile. Tahapan pra-pemrosesan meliputi case folding, tokenizing, stopword removal, dan stemming. Representasi fitur menggunakan metode Term Frequency–Inverse Document Frequency (TF-IDF). Evaluasi kinerja model dilakukan menggunakan metrik accuracy, precision, recall, dan F1-score. Hasil penelitian menunjukkan bahwa penerapan SMOTE mampu meningkatkan kinerja klasifikasi, khususnya pada kelas minoritas, dibandingkan dengan SVM tanpa penyeimbangan data. Dengan demikian, SMOTE terbukti efektif untuk meningkatkan performa analisis sentimen pada data tidak seimbang.
Co-Authors Ahmad Faqih Akbar, Miftahul Antika, Rindi APIPAH, KUSNATUL Ash Shiddiqi, Jimly Ayuningsih, Sri Fahreza, Rheznandya Fazari Hidayat, Nizar Gifthera Dwilestari Hayati, Umi Hoerunnisa, Anis Indriya Efendi, Dendy Irfan Ali, Irfan Khaerul Anam Khairul Akmal, Khairul khusnul khotimah Komalasari, Cahyaningrum Martanto Martanto . Muhammad Faisal Narasati, Riri Narasati Nasakh Nining Rahaningsih Nugroho, Ridho Nurhalizah, Shafa Aristia Pamungkas, Vicky Pratama, Denni Putri Ekawati Darma Raditya Danar Dana Rano Rhamadanti, Anisa Ridho Nugraha Rifa'i, Ahmad Rifa’I, Ahmad Rinaldi Dikananda, Arif Sadikin, Abdurrahman Saeful Anwar, Saeful Siti Latifah Sulaeman, Muhamad Sunana, Heliyanti Susana, Heliyanti Wahyudin, Edi