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Penerapan Teknologi Artificial Intelligence Sebagai Media Inovatif dalam Mendukung Proses Pembelajaran dan Kreativitas Siswa Paket C di PKBM Bangkit Kota Semarang Fikriah, Fari Katul; Ariyanto, Amelia Devi Putri
Jurnal Pengabdian Masyarakat Nusantara (JPMN) Vol. 5 No. 1 (2025): Februari - Juli 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jpmn.v5i1.4595

The rapid development of information technology requires educational institutions, including Community Learning Activity Centers (PKBM), to innovate in improving the quality of learning and fostering student creativity. PKBM Bangkit Kota Semarang as a non-formal educational institution still faces obstacles in the form of low learning motivation and limited technology-based learning media. This community service activity aims to design and implement Artificial Intelligence (AI) technology as an innovative media to support the learning process and encourage the creativity of Package C students. The implementation method includes identifying partner needs, designing and developing AI media, training and mentoring, implementing in class, and monitoring and evaluation. The results of the community service show that AI-based learning media has succeeded in increasing learning participation, facilitating access to materials, encouraging independent learning, and receiving positive responses from tutors and students. These findings demonstrate that the application of AI in PKBM has the potential to be an innovative solution for strengthening the quality of non-formal education in the digital era.

Penerapan Teknologi Artificial Intelligence Sebagai Media Inovatif dalam Mendukung Proses Pembelajaran dan Kreativitas Siswa Paket C di PKBM Bangkit Kota Semarang Fikriah, Fari Katul; Ariyanto, Amelia Devi Putri
Jurnal Pengabdian Masyarakat Nusantara (JPMN) Vol. 5 No. 1 (2025): Februari - Juli 2025
Publisher : Lembaga Otonom Lembaga Informasi dan Riset Indonesia (KITA INFO dan RISET)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35870/jpmn.v5i1.4595

The rapid development of information technology requires educational institutions, including Community Learning Activity Centers (PKBM), to innovate in improving the quality of learning and fostering student creativity. PKBM Bangkit Kota Semarang as a non-formal educational institution still faces obstacles in the form of low learning motivation and limited technology-based learning media. This community service activity aims to design and implement Artificial Intelligence (AI) technology as an innovative media to support the learning process and encourage the creativity of Package C students. The implementation method includes identifying partner needs, designing and developing AI media, training and mentoring, implementing in class, and monitoring and evaluation. The results of the community service show that AI-based learning media has succeeded in increasing learning participation, facilitating access to materials, encouraging independent learning, and receiving positive responses from tutors and students. These findings demonstrate that the application of AI in PKBM has the potential to be an innovative solution for strengthening the quality of non-formal education in the digital era.

Emotion Detection Using Contextual Embeddings for Indonesian Product Review Texts on E-commerce Platform Ariyanto, Amelia Devi Putri; Fari Katul Fikriah; Arif Fitra Setyawan
Pixel :Jurnal Ilmiah Komputer Grafis Vol. 17 No. 1 (2024): Pixel :Jurnal Ilmiah Komputer Grafis dan Ilmu Komputer
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/pixel.v17i1.2010

The advancement of e-commerce has changed the way people shop. However, there is a mismatch between the actual quality of a product and the seller’s description. Product reviews are an important source of information for making purchasing decisions. However, processing large numbers of reviews manually is difficult. This research aims to detect emotions in Indonesian language product review texts using contextual embeddings. The public dataset used was PRDECT-ID, which comprises five emotion labels. The methods used include data preprocessing, feature extraction using contextual embeddings such as Bidirectional Encoder Representations from Transformers (BERT), and classification using Decision Tree, Naïve Bayes, and k-Nearest Neighbors (KNN). Among the compared models, the KNN model demonstrated the highest improvement, achieving a 15.09% enhancement over the decision tree results. This research provides insights into the effectiveness of contextual embeddings in detecting emotions in Indonesian language product review texts.

Analisis Metode Estimasi Biaya pada Perangkat Lunak Beserta Faktor-Faktor yang Mempengaruhi : A Systematic Literature Review Ariyanto, Amelia Devi Putri; â€˜Azizah, Lutfiyatul; Yuhana, Umi Laili
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 9 No 4: Agustus 2022
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2021864611

Estimasi biaya sampai sekarang masih menjadi salah satu permasalahan utama dalam perencanaan proyek perangkat lunak. Estimasi biaya ini memiliki peran yang penting karena berpengaruh pada berjalannya proyek dan menjadi penentu keberhasilan suatu proyek perangkat lunak. Kegagalan estimasi biaya dalam perencanaan proyek perangkat lunak dapat menyebabkan proyek tidak berjalan dengan baik dan menimbulkan kerugian bagi perusahaan. Oleh karena itu, banyak peneliti sampai saat ini masih mencari dan melakukan penelitian untuk mendapatkan estimasi terbaik. Berbagai metode diusulkan untuk mendapatkan ketepatan akurasi dengan memperhatikan faktor-faktor estimasi biaya. Tujuan penelitian ini adalah membuat Systematic Literature Review (SLR) yang berisi rangkuman dan analisis perkembangan penelitian terbaru tentang estimasi biaya pada perangkat lunak, khususnya pada metode yang digunakan serta faktor-faktor yang mempengaruhi. Penelitian ini berhasil mengkaji 21 penelitian lain dalam lima tahun terakhir (2015-2020) dan didapatkan 24 metode usulan yang terbagi menjadi tiga jenis metode yang sering digunakan dalam melakukan estimasi biaya perangkat lunak yaitu nonparametrik, parametrik dan semiparametrik. Selain itu, penelitian ini juga berhasil menemukan metode dan kombinasi metode terbaik berdasarkan ketepatan akurasi beserta lima faktor utama yang mempengaruhi estimasi biaya sehingga dapat digunakan para peneliti atau praktisi lain untuk mengembangkan estimasi biaya pada proyek perangkat lunak. AbstractCost estimation has an important role because it affects the project’s progress and determines the success of a software project. Failure to estimate costs in software project planning can cause the project to not run well and cause losses to the company. Therefore, many researchers are still looking for and researching to get the best estimation by considering the cost estimation factors. The purpose of this study is to create a Systematic Literature Review (SLR) which contains a summary and analysis of the latest research developments on cost estimation in software, especially in the methods used and the factors that affect cost estimation. This study successfully reviewed 21 other studies in the last five years (2015-2020) and obtained 24 planning methods which are divided into three types of methods that are often used in conducting software cost research, namely nonparametric, parametric and semiparametric. Besides, this study also succeeded in finding the best method and combination of methods based on best accuracy, namely COCOMO II and the combination of Genetic Algorithm and Artificial Bee Colony, along with the five main factors that influence cost estimation so that it can be used by researchers or other practitioners to develop cost estimates for software projects.

CLASSIFICATION OF DENGUE FEVER DISEASE USING A MACHINE LEARNING-BASED RANDOM FOREST ALGORITHM ARIF FITRA SETYAWAN; Amelia Devi Putri Ariyanto; Fari Katul Fikriah
JIKO (Jurnal Informatika dan Komputer) Vol 7 No 2 (2024)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8496

Dengue Hemorrhagic Fever (DHF) is a tropical disease that often results in high morbidity and mortality rates. Early diagnosis of DHF is crucial to mitigate its adverse effects. However, manual diagnostic processes are often inefficient and prone to errors. This study aims to develop a DHF classification model using the Random Forest algorithm, which is expected to assist in the early diagnosis of this disease. The methodology used in this research is CRISP-DM (Cross-Industry Standard Process for Data Mining), which includes the stages of Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. Data was obtained from kaggle.com, and during the Data Preparation stage, missing values were removed, categorical features were encoded, data was normalized, and split into training and testing sets. The research results show that the Random Forest model has an accuracy of 88.5%, precision of 88.2%, recall of 65.2%, F1-score of 74.9%, and ROC AUC of 0.810. Feature importance analysis revealed that the Gender_Male and Body_Pain features have the largest contributions in DHF classification. Although the model demonstrated high accuracy and precision, the lower recall value indicates that some positive cases were missed, requiring further improvements. The Random Forest can be used as a tool for early DHF diagnosis, but further adjustments are necessary to enhance its performance. This research provides insights into the contributing factors for DHF diagnosis and the practical application potential of this model in medical decision support systems.

NAÏVE BAYES AND SUPPORT VECTOR MACHINE BASED ON OPTIMIZATION FOR PUBLIC SENTIMENT ANALYSIS POST-2024 ELECTION Fari Katul Fikriah; Amelia Devi Putri Ariyanto
JIKO (Jurnal Informatika dan Komputer) Vol 8 No 2 (2025)
Publisher : Program Studi Teknik Informatika Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v8i2.10147

The 2024 election has sparked an explosion of public opinion across various digital platforms, but the complexity and large volume of data make it difficult for policymakers to understand public sentiment in a timely manner. Therefore, an accurate and efficient sentiment analysis method is needed to automatically classify public opinion. This study aims to analyze and compare the performance of the Naïve Bayes algorithm and an optimized Support Vector Machine (SVM) in classifying post-election public sentiment. The research method includes collecting 10,000 text data entries from various data sources, conducting text preprocessing, extracting features using the TF-IDF method, applying both algorithms with parameter tuning, and generating their performance using accuracy, precision, recall, and F1 score metrics. The results show that the optimized SVM algorithm delivers superior performance, achieving 88.24% accuracy, compared to 82.35% for Naïve Bayes. These findings indicate that SVM is more effective in handling complex public opinion sentiment classification, thus serving as a valuable reference for post-election policymaking

KLASIFIKASI HASIL MRI TUMOR OTAK DENGAN EKTRAKSI FITUR GRAY LEVEL CO-OCCURANCE MATRIX (GLCM) Fari Katul Fikriah; Amelia Devi Putri Ariyanto; Arif Fitra Setyawan
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 9 No 2 (2024): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v9i2.4793

Bagian penting dari tubuh adalah otak yang mana menjadi sumber dari semua alat tubuh yang terletak dalam rongga tengkorak, tumor otak merupakan salah satu penyakit yang dapat menyerangnya. Pendeteksian tumor otak adalah salah satu aspek yang dinilai penting dalam diagnosa medis. Pada penelitian ini memiliki tujuan melakukan implementasi ekstraksi fitur GLCM (Gray Level Co-occurence Matrix) pada citra MRI tumor otak serta mencari performa algoritma yang paling baik dari deteksi tumor otak menggunakan citra MRI ini. Data yang dipakai pada penelitian ini merupakan data public yang berasal dari kaggle.com. Proses ekstraksi fitur pada citra digunakan pada penelitian ini GLCM yang mana memiliki fungsi menghitung frekuensi dari nilai intensitas piksel yang berjarak antar citra dengan menggunakan parameter 0o, 45o, 90o, 135o. Tahap selanjutnya pada penelitian ini adalah dengan melakukan langkah preprocessing dengan selanjutnya mencari nilai klasifikasi dari hasil MRI menggunakan algoritma NaÃ¯ve Bayes, C4.5 dan Neural Network. Hasil yang didapatkan memperlihatkan bahwa NaÃ¯ve Bayes memiliki performa algoritma paling baik dibandingkan C4.5 dan Neural Network yaitu dengan akurasi algoritma NaÃ¯ve Bayes sebesar 96.8%, sedangkan untuk algoritma C4.5 sebesar 41.5% dan Neural Network sebesar 38.25%. selain hal tersebut pada penelitian ini membuktikan bahwa dengan ekstraksi fitur GLCM terbukti efektif dalam menangkap informasi tekstur dari citra MRI yang sangat penting pada klasifikasi tumor otak.

Strengthening Madrasah Administrative Governance Through Cloud Storage and Notion-Based Collaborative Workspace: A Digital Transformation Initiative for Madrasah Aliyah Fari Katul Fikriah; Amelia Devi Putri Ariyanto
Jurnal Pengabdian UNDIKMA Vol. 7 No. 3 (2026): August
Publisher : LPPM Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jpu.v7i3.20762

This community service program aims to design and implement a cloud storage–based administrative management model integrated with a collaborative workspace using Notion for madrasahs affiliated with the Semarang City Private Madrasah Family Association (PKMAS). The program adopted a participatory approach consisting of needs assessment, model development, implementation through training and mentoring, evaluation, and monitoring to ensure the achievement of program objectives. The program resulted in an integrated, practical, and readily adoptable digital administrative management model for madrasahs. In addition, it enhanced the digital competencies of educational staff and promoted more collaborative administrative work practices. The implementation of the program led to improvements in participants' knowledge, digital skills, and administrative management efficiency. Evaluation results demonstrated increased participant competence following the training, with the mean understanding score increasing from 2 to 5 in digital administration, cloud storage utilization, and collaborative work, and from 0 to 4 in the use of Notion. Furthermore, 93.9% of participants reported satisfaction with the program. The program was implemented across 13 PKMAS-affiliated madrasahs and involved 33 participants, demonstrating its potential to support more integrated, efficient, and collaborative administrative management.

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