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All Journal Jurnal Ilmu Komputer Pixel : Jurnal Ilmiah Komputer Grafis Elkom: Jurnal Elektronika dan Komputer CommIT (Communication & Information Technology) Jurnal Transformatika RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Komtika (Komputasi dan Informatika) Jiko (Jurnal Informatika dan komputer) Jurnal Informasi dan Komputer Jurnal Pengabdian UNDIKMA LOSARI: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Nusantara Jurnal Abdi Masyarakat Indonesia Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Bulletin of Information Technology (BIT) Jurnal Inovasi Pengabdian dan Pemberdayaan Masyarakat SmartComp Jurnal Pengabdian Masyarakat Tekno Jurnal Komtika (Komputasi dan Informatika) Journal of Computing and Smart Ecosystems
Fari Katul Fikriah
Universitas Wahid Hasyim
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Transportation Other

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TOGAF ADM pada Enterprise Architecture Planning untuk Sistem Informasi Manajemen Terintegrasi Fanani, M. Rudi; Fikriah, Fari Katul
Smart Comp :Jurnalnya Orang Pintar Komputer Vol 11, No 2 (2022): Smart Comp: Jurnalnya Orang Pintar Komputer
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/smartcomp.v11i2.3434

Abstract

Institut merupakan sebuah perusahaan yang bergerak di bidang pendidikan. Sistem informasi manajemen terintegrasi sangat dibutuhkan karena dapat terkait antara satu sistem dengan sistem lainnya, dimana masing-masing sistem bisa saling berbagi basis data yang sama dalam waktu bersamaan sebagai upaya dukungan untuk menjalankan aktifitas dan tata kelola perguruan tinggi. Perencanaan pembangunan system informasi dapat memperkecil kegagalan dalam penerapannya. Enterprise Architecture Planning (EAP) berbentuk blueprint pada seluruh unit dan untuk setiap sub unit pada perguruan tinggi. Tujuan penelitian ini akan membahas perancangan enterprise architecture yang sesuai pada fungsi kegiatan bisnis ITS NU pekalongan Dalam melakukan perancangan EA pada penelitian ini menggunakan TOGAF ADM yang terdiri dari fase preliminary phase, architecture vision, business architecture, information system architecture, technology architecture. Dari penelitian ini akan dihasilkan output berupa blueprint dimana mempunyai Keunggulan dari TOGAF ADM adalah penyelesaian proses, fleksibilitas dalam penggunaan elemen, integrasi atau interkoneksi antar lapisan, netralitas vendor serta keselarasan dengan standar industry. Kekurangan TOGAF ADM adalah Sebagian besar difokuskan pada teknologi, tidak ditekankan pada arsitektur informasi dan aplikasi. Enterprise Architecture yang dihasilkan dapat mempermudah para pengguna dalam pelaksanaan aktivitas unit keuangan, unit kepegawaian, unit umum, unit sistem informasi, unit publikasi, unit penerimaan mahasiswa baru, unit akademik dan wisuda yang sesuai dengan Fungsi ITS NU Pekalongan dalam hal peningkatan fungsi bisnis, sistem informasi, dan teknologi informasi yang digunakan.
Integrating Shortest Job First (SJF) Scheduling with Neural Networks for Enhanced Predictive Process Scheduling Aditya Putra Ramdani; Midda Restia Primadani; Fari Katul Fikriah; Atika Mutiarachim
Journal of Computing and Smart Ecosystems Vol. 1 No. 1 (2025): J-CaSE
Publisher : S1 Teknologi Informasi, Universitas Muhammadiyah Semarang

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

Abstract

Process scheduling is a critical component of operating systems, directly influencing CPU utilization and overall system efficiency. The Shortest Job First (SJF) algorithm is theoretically optimal in minimizing average waiting time but is limited by its dependence on accurate burst time estimation. This study proposes a hybrid scheduling approach that integrates neural networks (NN) with SJF to dynamically predict process execution times. The neural model was trained on process-level features, including CPU usage, memory usage, priority, and arrival time, and its predictions were embedded into the SJF mechanism. Simulation results demonstrate that the NN-enhanced SJF achieves notable reductions in average waiting time and turnaround time while improving CPU utilization compared to traditional SJF and Round Robin algorithms. These findings highlight the practical viability of lightweight predictive models for enhancing classical scheduling techniques and extend their applicability to dynamic and heterogeneous computing environments.
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

Abstract

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.
UNDERSTANDING PUBLIC OPINION ON POLITICAL CANDIDATES THROUGH TWITTER SENTIMENT ANALYSIS: A COMPARATIVE STUDY OF FEATURE EXTRACTION Amelia Devi Putri Ariyanto; Fari Katul Fikriah
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.v8i2.9993

Abstract

Presidential elections are crucial in a country's political dynamics and are increasingly discussed on social media platforms like Twitter. However, sentiment analysis of public opinion on these platforms faces significant challenges, such as large data volumes, diverse formats, and the complexity of informal language. The key challenge is choosing the most appropriate feature extraction technique and classification algorithm to address the unique characteristics of Indonesian-language tweets in the context of presidential elections. This study aims to compare the effectiveness of two feature extraction approaches—semantic based on BERT (Bidirectional Encoder Representations from Transformers) and statistical based on TF-IDF (Term Frequency-Inverse Document Frequency)—in sentiment analysis of Indonesian-language tweets related to the presidential election, using four classification algorithms: Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors, and Decision Tree. The experimental results demonstrate that the combination of TF-IDF with SVM provides the best performance, with an accuracy of 85.1% and a macro f1-score of 0.81, outperforming the BERT approach used statically. These findings indicate that statistical approaches such as TF-IDF remain relevant and practical for short social media texts and emphasize the importance of choosing a method that suits the characteristics of the data and the context of the analysis.
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

Abstract

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

Abstract

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 Naïve Bayes, C4.5 dan Neural Network. Hasil yang didapatkan memperlihatkan bahwa Naïve Bayes memiliki performa algoritma paling baik dibandingkan C4.5 dan Neural Network yaitu dengan akurasi algoritma Naïve 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.
Pelatihan Desain Grafis Siswa PKBM Bangkit di Kota Semarang untuk Kemandirian Ekonomi Kreatif Amelia Devi Putri Ariyanto; Fari Katul Fikriah
Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Vol. 5 No. 5 (2025): September 2025 - Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/altifani.v5i5.750

Abstract

Perkembangan ekonomi digital menuntut penguasaan keterampilan desain grafis sebagai bagian dari strategi komunikasi visual. Kegiatan pengabdian ini bertujuan untuk meningkatkan keterampilan desain grafis dasar siswa PKBM Bangkit di Kota Semarang guna mendukung kemandirian ekonomi kreatif. Metode pelaksanaan meliputi identifikasi kebutuhan, perencanaan berbasis praktik, pelatihan penggunaan aplikasi Canva, dan evaluasi partisipatif. Pelatihan dilakukan secara interaktif dan aplikatif, dengan materi yang mudah diakses dan disesuaikan dengan konteks kehidupan peserta. Hasil menunjukkan bahwa sebagian besar peserta merasa Canva mudah digunakan, dan memahami materi dengan cukup jelas. Peserta mampu menghasilkan desain sederhana untuk kebutuhan pribadi dan komunitas. Kegiatan ini membuktikan bahwa PKBM memiliki potensi sebagai wadah pengembangan keterampilan digital yang inklusif. Temuan ini memperkuat pentingnya integrasi pelatihan kreatif dalam pendidikan nonformal untuk mendukung pemberdayaan ekonomi masyarakat secara berkelanjutan.
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

Abstract

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.
Boosting Performance Klasifikasi kNN Customer Loyalty dengan Chi-Square dan Information Gain Atika Mutiarachim; Fari Katul Fikriah; Basirudin Ansor; Aditya Putra Ramdani
Jurnal Transformatika Vol. 22 No. 2 (2025): January 2025
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/6wgy1097

Abstract

Understanding customer purchasing behavior is essential for predicting customer loyalty, which directly impacts a company's long-term success. This research aims to determine the effect of chi-square and information gain feature selection in optimizing customer loyalty classification performance, compared to pure kNN. Using a public customer purchasing behavior dataset from Kaggle, containing 10,000 data, 12 attributes with loyalty_status as the label (Gold, Regular, Silver). Evaluating performance by accuracy, kappa, classification error, recall, precision, and RMSE. The highest accuracy 91.99% was obtained by kNN k=3 with information gain, kappa 0.844, precision 95.44%, recall 86.30%, with the lowest classification error 8.01% and the second lowest RMSE 0.245, after kNN k=3 with chi-square. Results show that feature selection has a positive impact on classification, increasing accuracy and reducing errors, with the combination of the kNN k=3 method and information gain proving successful in obtaining high accuracy in classifying customer loyalty.
Co-Authors Aditya Putra Ramdani Aditya Putra Ramdani Amelia Devi Putri Ariyanto Amelia Devi Putri Ariyanto Amelia Devi Putri Ariyanto Amelia Devi Putri Ariyanto Andriansah, Zulfi Arif Fitra Setyawan Arif Fitra Setyawan Arif Fitra Setyawan Arip Kristiyanto Ariyanto , Amelia Devi Putri Atika Mutiarachim Atika Mutiarachim Basirudin Ansor Burhanis Sulthan, M Devi Putri Ariyanto, Amelia Fanani, M. Rudi Fitra Setiawan, Arif Husni Hidayat Inkiriwang, Rully Khoirur Roziqin, Moh. M Burhanis Sulthan M. Rudi Fanani Midda Restia Primadani Moh. Khoirur Roziqin Mujahidah, Nailatul Nailatul Mujahidah Nugaraha, Rozaq Isnaini Nur Hadian Rizal, Muhamad Rizaludin Rozaq Isnaini Nugraha Setyawan, Arif Fitra Verawati, Liesta