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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Jurnal Informatika dan Teknik Elektro Terapan Jurnal Eksplora Informatika JURNAL PENDIDIKAN TAMBUSAI JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jusikom: Jurnal Sistem Informasi Ilmu Komputer CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Jifosi PLAKAT : Jurnal Pelayanan Kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Ilmiah Teknologi Informasi dan Robotika International Journal Of Computer, Network Security and Information System (IJCONSIST) Digital Transformation Technology (Digitech) Saber: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi International Journal of Information Engineering and Science
Ani Dijah Rahajoe
Bhayangkara University, Indonesia
Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Social Sciences

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IMPLEMENTASI METODE COLLABORATIVE FILTERING MENGGUNAKAN ALGORITMA COSINE SIMILARITY DAN JACCARD SIMILARITY PADA SISTEM E-COMMERCE Waskito, Muhammad Rizal; Rahajoe, Ani Dijah; Nurlaili, Afina Lina
Jurnal Informatika dan Teknik Elektro Terapan Vol 12, No 3S1 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3S1.5315

Abstract

Peningkatan pesat e-commerce telah mengubah pola belanja konsumen, menciptakan kebutuhan akan sistem rekomendasi produk yang lebih personal dan relevan. Tantangan yang sering muncul adalah ketidakmampuan sistem rekomendasi dalam menangani data sparsity dan memberikan rekomendasi yang akurat, terutama ketika data pengguna terbatas. Untuk mengatasi tantangan ini, penelitian ini mengimplementasikan metode Collaborative Filtering dengan algoritma Cosine Similarity dan Jaccard Similarity. Cosine Similarity digunakan untuk mengukur kemiripan antar pengguna berdasarkan nilai yang diberikan terhadap produk, sementara Jaccard Similarity fokus pada kesamaan interaksi pengguna tanpa memperhatikan nilai rating. Hasil penelitian menunjukkan bahwa Cosine Similarity cenderung memberikan skor kemiripan yang lebih tinggi dibandingkan Jaccard Similarity, terutama dalam situasi data yang tidak lengkap, dengan rata rata selisih skor sebesar 26.14%. Selain itu, sistem yang dikembangkan mampu memanfaatkan efek Fear of Missing Out (FoMO) untuk meningkatkan relevansi dan urgensi pembelian produk. Integrasi algoritma ini meningkatkan akurasi rekomendasi dan membuka peluang untuk pengembangan lebih lanjut, seperti penerapan hybrid filtering, guna mengoptimalkan kinerja sistem rekomendasi dalam e-commerce.
Artificial Intelligence-Based Expert System for Recirculating Aquaculture Systems in Tulungagung Regency, East Java Rahajoe, Ani Dijah; Sanjaya, Yushinta Aristina; Fauziah, Anna; Zakaria, Rino; Wibowo, Arif Setyo
Plakat : Jurnal Pelayanan Kepada Masyarakat Vol 7, No 1 (2025): Volume 7, Nomor 1, Juni Tahun 2025
Publisher : Fakultas Ilmu Sosial dan Ilmu Politik, Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/plakat.v7i1.18861

Abstract

Freshwater fish farming in Indonesia plays a crucial role in supporting food security and the economy. UD Tirta Mas Agung Abadi, based in East Java, is a key player in the breeding and cultivation of freshwater ornamental fish, as well as providing feed and aquaculture equipment. The main challenge lies in the complexity of parameters in the recirculating aquaculture system (RAS), such as weather, water quality, temperature, pH, dissolved oxygen, and other chemical contents. The reliance on experts also makes it difficult for farmers to take quick and measured actions in aquaculture management. The first solution proposed is the development of an intelligent system application based on artificial intelligence (expert systems) to efficiently manage RAS parameters. Trials have shown that 95% of respondents found the application met their needs. The second solution involves enhancing the capacity of freshwater ornamental fish farmers through training and mentoring, with 90% of respondents successfully implementing new techniques at their farming locations. These innovations have the potential to improve the efficiency and sustainability of freshwater fish farming in Tulungagung Regency. Budidaya ikan air tawar di Indonesia memegang peranan penting dalam mendukung ketahanan pangan dan perekonomian. UD Tirta Mas Agung Abadi, yang berbasis di Jawa Timur, merupakan salah satu pelaku utama dalam pembenihan dan pembesaran ikan hias air tawar, serta menyediakan pakan dan peralatan budidaya. Permasalahan utama yang dihadapi adalah kompleksitas parameter dalam sistem akuakultur resirkulasi (RAS) seperti cuaca, kualitas air, suhu, pH, oksigen terlarut, dan kandungan kimia lainnya. Ketergantungan pada pakar juga menyulitkan petani untuk menentukan tindakan budidaya yang cepat dan terukur. Solusi pertama yang ditawarkan adalah pengembangan aplikasi sistem cerdas berbasis kecerdasan buatan (sistem pakar) untuk mendukung pengelolaan parameter RAS secara efisien. Uji coba menunjukkan 95% responden merasa aplikasi ini memenuhi kebutuhan mereka. Solusi kedua adalah peningkatan kapasitas petani ikan hias air tawar melalui pelatihan dan pendampingan, dengan hasil 90% responden berhasil mempraktikkan teknik baru di lokasi budidaya mereka. Inovasi ini berpotensi meningkatkan efisiensi dan keberlanjutan budidaya ikan air tawar di Kabupaten Tulungagung.
Hybrid Neural Network-Based Road Damage Detection Using CNN-RNN and CNN-MLP Models Rahajoe, Ani Dijah; Suriansyah, Muhammad; Jr, Angelo A. Beltran
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4435

Abstract

Currently, there are many applications of image processing in various fields. One of them is the recognition of paved road images. Detection through images helps in handling infrastructure development roads. With the advancement of technology, especially in the field of deep learning, the process of detecting road damage can be done automatically and more efficiently. The road damage detection system can be integrated into the smart city system to monitor infrastructure conditions in real time. This study will use a combined deep learning algorithm between Convolutional Neural Network- Recurrent Neural Network (CNN-RNN) and as a comparison using Convolutional Neural Network- MultiLayer Perceptrons (CNN-MLP). The study aims to analyze the accuracy of using the CNN-RNN and CNN-MLP algorithms for detecting paved roads that have categories of undamaged roads, damaged roads, and damaged roads with holes. The detection of paved roads has complex details so an algorithm that has good performance with high accuracy is needed. The results of the study showed that the CNN-RNN hybrid had a better accuracy of 96.59 percent than the CNN-MLP hybrid model of 95.9 percent.  
KOMBINASI RANK ORDER CENTROID (ROC) DAN MULTI ATTRIBUTE UTILITY THEORY (MAUT) UNTUK SELEKSI CALON PENERIMA KIP-KULIAH Chaurina, Agfanadita Rezkia; Rahajoe, Ani Dijah; Nurlaili, Afina Lina
Jurnal Informatika dan Teknik Elektro Terapan Vol. 13 No. 3 (2025)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v13i3.6641

Abstract

The low participation rate in higher education in Indonesia remains a major challenge, particularly among students from underprivileged socioeconomic backgrounds. Programs such as KIP-Kuliah are part of the government's efforts to expand educational access, although various implementation challenges persist. At Universitas Pembangunan Nasional “Veteran” Jawa Timur, the number of KIP-Kuliah applicants consistently exceeds the available quota each year, necessitating a selection system that supports fair and accurate decision-making. This study aims to develop a web-based decision support system to assist in the selection process of KIP-Kuliah recipients more systematically. The method combines Rank Order Centroid (ROC) to assign weights to each criterion based on priority ranking and Multi-Attribute Utility Theory (MAUT) to calculate the final score for each alternative. The ranking results are then adjusted according to the recipient quota set for each study program accreditation. Evaluation results show that the system achieved a 96.67% accuracy rate compared to the institution’s manual selection results, indicating its effectiveness in supporting decision-making that is objective, efficient, and transparent.
Optimasi Model Prediksi Kelulusan Mahasiswa Berbasis Principal Component Analysis dan Modified K-Nearest Neighbor Pramnesti, Adisty Regina; Rahajoe, Ani Dijah; Mumpuni, Retno
CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Vol 11 No 2 (2025): CICES
Publisher : UNIVERSITAS RAHARJA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33050/cices.v11i2.3914

Abstract

Angka drop-out mahasiswa di perguruan tinggi masih menjadi permasalahan serius karena berdampak pada pemborosan sumber daya dan perkembangan institusi. Identifikasi dini terhadap mahasiswa berisiko drop-out sangat penting, namun metode manual dan algoritma K-Nearest Neighbor (KNN) konvensional masih memiliki keterbatasan, seperti sensitivitas terhadap outlier dan data berdimensi tinggi. Penelitian ini mengusulkan integrasi Principal Component Analysis (PCA) dan Modified K-Nearest Neighbor (MKNN) untuk meningkatkan akurasi klasifikasi kelulusan mahasiswa. PCA digunakan untuk mereduksi 14 variabel menjadi 2 variabel utama, sedangkan MKNN memodifikasi KNN dengan teknik weight voting berbasis jarak serta validasi data latih guna mengurangi perngaruh outlier. Model diujikan dengan skema pembagian data 60:40 (latih:uji) dan parameter optimal k=9. Hasil penelitian menunjukkan bahwa kombinasi PCA dan MKNN mampu mencapai akurasi 99,31%, meningkat 0,93% dibanding KNN standar, serta menghasilkan presisi, recall, dan F1-Score sebesar 99,3%. Temuan ini menegaskan bahwa integrasi reduksi dimensi dan weight voting efektif dalam meningkatkan kinerja klasifikasi, sehingga model ini berpotensi menjadi alat prediksi drop-out yang andal di lingkungan pendidikan tinggi.
REAL-TIME DROWSY FACE DETECTION FOR ONLINE LEARNING BASED ON RANDOM FOREST AND DECISION TREE ALGORITHMS Ani Dijah Rahajoe; Subekti, Mohamad Rafli Agung; Suriansyah, Muhammad
Jurnal Teknik Informatika (Jutif) Vol. 4 No. 6 (2023): JUTIF Volume 4, Number 6, Desember 2023
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2023.4.6.1554

Abstract

In the current era, technology regarding artificial intelligence has developed rapidly and has been used in various areas of life. Face detection is one of the applications of Artificial Intelligence. This research aims to detect students' faces during the online learning process and succeeded in getting positive feedback when tested on students. Student detection includes drowsy and alertness. The method is via webcam in real-time so that the screen will show whether the student is drowsy or alert. In the trial, the teacher can find out who is in a drowsy and alert condition. On the other hand, students can find out that they fall into the drowsy or alert category. So that both parties immediately respond to what should be done based on the classification results. The algorithms used are Decision Tree and Random Forest. The accuracy results of the Random Forest algorithm are better than the Decision Tree algorithm, namely 65 percent, while the Decision Tree algorithm is 58 percent. The division of training data and test data uses a Kfold of 5. When Kfold is equal to 2, both algorithms have the highest accuracy, where Random Forest has an accuracy of 85 percent, and Decision Tre has an accuracy of 65 percent.
IMPLEMENTASI METODE COLLABORATIVE FILTERING MENGGUNAKAN ALGORITMA COSINE SIMILARITY DAN JACCARD SIMILARITY PADA SISTEM E-COMMERCE Waskito, Muhammad Rizal; Rahajoe, Ani Dijah; Nurlaili, Afina Lina
Jurnal Informatika dan Teknik Elektro Terapan Vol. 12 No. 3S1 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3S1.5315

Abstract

Peningkatan pesat e-commerce telah mengubah pola belanja konsumen, menciptakan kebutuhan akan sistem rekomendasi produk yang lebih personal dan relevan. Tantangan yang sering muncul adalah ketidakmampuan sistem rekomendasi dalam menangani data sparsity dan memberikan rekomendasi yang akurat, terutama ketika data pengguna terbatas. Untuk mengatasi tantangan ini, penelitian ini mengimplementasikan metode Collaborative Filtering dengan algoritma Cosine Similarity dan Jaccard Similarity. Cosine Similarity digunakan untuk mengukur kemiripan antar pengguna berdasarkan nilai yang diberikan terhadap produk, sementara Jaccard Similarity fokus pada kesamaan interaksi pengguna tanpa memperhatikan nilai rating. Hasil penelitian menunjukkan bahwa Cosine Similarity cenderung memberikan skor kemiripan yang lebih tinggi dibandingkan Jaccard Similarity, terutama dalam situasi data yang tidak lengkap, dengan rata rata selisih skor sebesar 26.14%. Selain itu, sistem yang dikembangkan mampu memanfaatkan efek Fear of Missing Out (FoMO) untuk meningkatkan relevansi dan urgensi pembelian produk. Integrasi algoritma ini meningkatkan akurasi rekomendasi dan membuka peluang untuk pengembangan lebih lanjut, seperti penerapan hybrid filtering, guna mengoptimalkan kinerja sistem rekomendasi dalam e-commerce.
Optimizing Red Onion TSS (True Shallod Seed) Production in the Lowlands Based on Smart Sensors Moeljani, Ida Retno; Rahajoe, RR Ani Dijah; N, Pangesti
IJCONSIST JOURNALS Vol 5 No 1 (2023): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v5i1.115

Abstract

Onion cultivation technology using seeds still needs to be developed and socialized at the farm level. to be socialized at the farmer level, considering that until now farmers still cultivate shallots with consumption seed bulbs because there are still not many TSS produced, especially in the lowlands.In principle, not all shallot varieties are capable of flowering, some shallot varieties are capable of flowering. flowering, some shallot varieties are only 30% capable of flowering. This problem can be solved by optimizing flowering with an automation system. The advancement of Internet of thing (IoT) technology can be applied to optimize flowering by using smart sensors on the onion. flowering by using smart sensors on several varieties of shallots. The lanchor blue variety had no flower bulbs that set fruit and produced TSS seeds. This is because all the flower bulbs of the lanchor blue variety were rotten/damaged by disease due to the use of high watering during the growth period that led to flowering, fertilization, and sprouting. There was no interaction between varieties and application of gibberellic acid + and packlobutrazol on seed yield of TSS (Table4). In Bauji and Maaserati varieties, the percentage of flower bulbs that bear fruit and seed (harvested) is still better than BiruLanchor, only about 59.68 to 70% of the total number of flower bulbs that grow (Table 4). This indicates that the process of fertilization and seed formation of shallots is not optimal.
Feature Engineering Optimization on the Performance of XGBoost, Random Forest, and Support Vector Regression Algoritms in House Price Prediction Trenggono, Brahmantio Widyo; Diyasa, I Gede Susrama Mas; Rahajoe, Ani Dijah
IJCONSIST JOURNALS Vol 6 No 1 (2024): September
Publisher : International Journal of Computer, Network Security and Information System

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/ijconsist.v6i1.149

Abstract

As the years go by, the ever-increasing movement of house prices has become an important factor in investment decisions and financial planning to curb inflation. However, fluctuations or increases in house prices can be caused by various factors that can affect the value of house price predictions. This study aims to analyze the influence of optimization and the relationship between feature engineering and modeling in house price predictions. The research stages include data preprocessing, logarithmic transformation, feature engineering, data splitting, and optimization in determining parameters during tuning. Model performance is evaluated using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Determination coefficient (R-Squared) metrics. The results show that the Support Vector Regression algorithm produces the best performance with a MAE value of 274 million, an RMSE of 780 million, a MAPE of 7%, and an R-Squared of 98%. This research is expected to serve as a reference for future studies on regression model optimization, particularly in decision-making for more accurate house price predictions.
Customer Data Management Analysis for Customer Segmentation Using K-Means Clustering Method Andre Leto; Reza Aminullah; Ani Dijah Rahajoe
International Journal of Information Engineering and Science Vol. 2 No. 4 (2025): November : International Journal of Information Engineering and Science
Publisher : Asosiasi Riset Teknik Elektro dan Infomatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/ijies.v2i4.345

Abstract

This study aims to examine customer segmentation through K-Means clustering from a customer data management perspective, emphasizing the interpretive value of analytical results rather than solely their computational outcomes. The research addresses a critical issue in contemporary data-driven organizations, where customer analytics is often reduced to technical modeling without sufficient translation into managerial insights. To respond to this gap, the study adopts a qualitative interpretive approach embedded within a quantitative clustering process, positioning clustering as part of a broader information management cycle. The empirical analysis is based on the Mall Customers Dataset obtained from Kaggle, consisting of 200 customer records with numerical attributes representing age, annual income, and spending score. Quantitative processing using K-Means clustering was employed to identify customer segments, while qualitative interpretation was applied to analyze the managerial meaning of each cluster. Data interpretation was supported by analytical documentation, visualization outputs, and reflective analysis of cluster characteristics. The findings reveal four distinct customer segments with different behavioral and economic profiles, each carrying specific strategic implications for customer relationship management and marketing decision-making. The study demonstrates that the primary value of clustering lies not merely in segment formation, but in its ability to transform raw customer data into actionable managerial knowledge. In conclusion, this research contributes to customer analytics literature by integrating data mining techniques with qualitative interpretation, offering a more human-centered and decision-oriented framework for customer data management. Future research is encouraged to extend this approach using organizational case studies or participatory decision-making contexts.
Co-Authors Agung Subekti, Mohamad Rafli Ainur Rahim Akash, Fransisco Rivaldi Al Afgany, Muhammad Iqbal Andre Leto Anna Fauziah arif arizal Arif Setyo Wibowo Azaidane, Dandi Bimantoro, Bisma Satrio Chaurina, Agfanadita Rezkia Daniar, Aninditya Dian Agus Prawinata Diyasa, I Gede Susrama Mas Eka Prakarsa Mandyartha Feriza, Reyana Dinda Maulan Fransiska, Amelia Henni Endah Wahanani I Gede Susrama Mas Diyasa Ida Retno Moeljani Indartono, Taqiyya Jr, Angelo A. Beltran M Mahaputra M. Mahaputra Made Hanindia Prami Swari Megantara, Sofia Ramadhani Muhammad Farhan Maulana Muttaqin, Faisal N, Pangesti Nurlaili, Afina Lina P. Eko Prasetyo Pahlevy, Mohammad Reza Perkasa, Laurensius Gading Surya Pramnesti, Adisty Regina Putra, Brian Akhdan Retno Mumpuni Reza Aminullah Safeyah, Muchlisiniyati Sanjaya, Yushinta Aristina Satrio Budi Wahyuono Soffiana Agustin, Soffiana Subekti, Mohamad Rafli Agung Suriansyah, Muhammad Suryantari, Putu Anggi Syariful Alim Trenggono, Brahmantio Widyo Waskito, Muhammad Rizal Winarko, Edi Zakaria, Rino