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All Journal Techno.Com: Jurnal Teknologi Informasi Telematika Scientific Journal of Informatics Jurnal Ilmiah KOMPUTASI Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Indonesian Journal of Artificial Intelligence and Data Mining INOVTEK POLBENG INOVTEK Polbeng - Seri Informatika Dinamisia: Jurnal Pengabdian Kepada Masyarakat JURNAL ILMIAH INFORMATIKA JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI ILKOM Jurnal Ilmiah MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer ComTech: Computer, Mathematics and Engineering Applications JOISIE (Journal Of Information Systems And Informatics Engineering) Building of Informatics, Technology and Science Jurnal Mantik Jurnal Informatika dan Rekayasa Elektronik Jurnal Teknologi Dan Sistem Informasi Bisnis Jurnal Sistem informasi dan informatika (SIMIKA) Journal of Applied Data Sciences Jurnal Computer Science and Information Technology (CoSciTech) Mitra Mahajana: Jurnal Pengabdian Masyarakat Jurnal J-PEMAS J-COSCIS : Journal of Computer Science Community Service JAIA - Journal of Artificial Intelligence and Applications Malcom: Indonesian Journal of Machine Learning and Computer Science SATIN - Sains dan Teknologi Informasi Jurnal Ilmiah Betrik : Besemah Teknologi Informasi dan Komputer The Indonesian Journal of Computer Science International Journal of Advances in Artificial Intelligence and Machine Learning
Efrizoni, Lusiana
Program Studi Teknik Informatika, STMIK Amik Riau
Agriculture, Biological Sciences & Forestry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Other

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Evaluasi Performa Aplikasi Gojek Melalui Klasifikasi Kata Ulasan Pengguna Dengan Metode SVM Putra, Febrianda; Ihsan, Raja Muhammad; Tahiyat, Hafsah Fulaila; Efrizoni, Lusiana; Rahmaddeni, Rahmaddeni
Techno.Com Vol. 23 No. 3 (2024): Agustus 2024
Publisher : LPPM Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/tc.v23i3.11379

Abstract

Aplikasi Gojek, sebagai salah satu aplikasi ride-hailing terkemuka di Indonesia, menghadapi tantangan berkelanjutan dalam mempertahankan dan meningkatkan kualitas layanan serta kepuasan pengguna. Penelitian ini bertujuan mengevaluasi performa aplikasi Gojek melalui analisis sentimen dari 200.000 ulasan pengguna yang dikumpulkan dari platform aplikasi selama periode 2021-2024, menggunakan metode Support Vector Machine (SVM). Fokus penelitian meliputi aspek layanan, keamanan, promosi, dan masalah teknis. Metode SVM dipilih karena kemampuannya dalam menangani klasifikasi dengan dataset kompleks. Data ulasan pengguna, yang ditulis dalam bahasa Indonesia, diolah dengan teknik pemrosesan teks dan fitur ekstraksi FastText, yang dikenal cepat dan akurat. Praproses data melibatkan tokenisasi, penghapusan stopword, dan teknik preprocessing lainnya. Kinerja model diukur dengan akurasi, presisi, recall, dan F1-score. Hasil menunjukkan bahwa FastText menawarkan waktu pelatihan cepat dengan akurasi kompetitif, sementara SVM unggul dalam presisi dan recall. Penerapan SVM dengan pembagian data 80:20 mencapai akurasi 91% dalam mengklasifikasikan sentimen pengguna. Temuan ini memberikan wawasan penting untuk pengembangan dan evaluasi aplikasi berbasis ulasan pengguna.
KLASIFIKASI TINGKAT RISIKO DIABETES MENGGUNAKAN ALGORITMA RANDOM FOREST Setiawan , Andri; Nasution , Zikri Hardyan; Khairi, Zuriatul; Rahmaddeni; Efrizoni, Lusiana
Jurnal Informatika dan Rekayasa Elektronik Vol. 7 No. 2 (2024): JIRE NOPEMBER 2024
Publisher : LPPM STMIK Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36595/jire.v7i2.1259

Abstract

Diabetes mellitus, penyakit metabolik kronis, menyebabkan kadar gula darah berlebihan karena tubuh tidak menghasilkan cukup insulin atau tidak mampu menggunakannya secara efektif. Kondisi ini biasanya disebabkan oleh masalah organ tubuh. Peningkatan glukosa darah yang berlarut-larut akibat diabetes dapat menyebabkan gagal ginjal, kebutaan, dan serangan jantung. Diabetes dinyatakan pada pasien yang memiliki glukosa darah yang lebih tinggi dari nilai normal. Organisasi Kesehatan Dunia (WHO) mengatakan bahwa pada tahun 2011, 4,6 juta orang meninggal karena diabetes, 2,2 juta karena kadar glukosa darah tinggi, dan 1,6 juta karena diabetes. Jumlah orang yang menderita diabetes pada tahun 2015 adalah 415 juta, dan diperkirakan akan meningkat menjadi 642 juta pada tahun 2040. Salah satu metode data mining yang membantu prediksi adalah klasifikasi data mining; tujuan penelitian ini adalah untuk membuat dan menerapkan klasifikasi ini. Penelitian ini juga menyelidiki epidemiologi diabetes, faktor risiko, strategi pencegahan dan pengelolaan diabetes, dan strategi untuk mengurangi beban penyakit diabetes di masa depan. Klasifikasi dapat dilakukan dengan algoritma Random Forest. Data tentang penyakit diabetes diolah oleh penulis melalui metode Random Forest. Hasil evaluasi model menunjukkan bahwa algoritma Forest Random dapat mengklasifikasikan risiko diabetes dengan akurat. Dari seleksi rangking variabel, ditemukan bahwa ada 300 pasien dengan risiko diabetes rendah dan 20 pasien dengan risiko diabetes tinggi. Kami juga menemukan bahwa model biasanya dapat mengklasifikasikan data dengan benar dengan nilai akurasi sebesar 98%, dan AUC dikategorikan sebagai “Excellent Classification" karena kemampuan untuk membedakan kelas positif dan negatif dengan nilai Area Under Curve (AUC) 100%.
Improved Performance of Hybrid GRU-BiLSTM for Detection Emotion on Twitter Dataset Anam, M. Khairul; Munawir, Munawir; Efrizoni, Lusiana; Fadillah, Nurul; Agustin, Wirta; Syahputra, Irwanda; Lestari, Tri Putri; Firdaus, Muhammad Bambang; Lathifah, Lathifah; Sari, Atalya Kurnia
Journal of Applied Data Sciences Vol 6, No 1: JANUARY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i1.459

Abstract

This study addresses emotion detection challenges in tweets, focusing on contextual understanding and class imbalance. A novel hybrid deep learning architecture combining GRU-BiLSTM with SMOTE is proposed to enhance classification performance on an Israel-Palestine conflict dataset. The dataset contains 40,000 tweets labeled with six emotions: anger, disgust, fear, joy, sadness, and surprise. SMOTE effectively balances the dataset, improving model fairness in detecting minority classes. Experimental results show that the GRU-BiLSTM hybrid with an 80:20 data split achieves the highest accuracy of 89%, surpassing BiLSTM alone, which obtained 88%, and other state-of-the-art models. Notably, the proposed model delivers significant improvement in detecting the emotion of joy (recall: 0.87, F1-score: 0.86). In contrast, the surprise category remains challenging (recall: 0.24). Compared to existing research, this study highlights the effectiveness of combining SMOTE and hybrid GRU-BiLSTM, outperforming models such as CNN, GRU, and LSTM on similar datasets. The incorporation of GloVe embeddings enhances contextual word representations, enabling nuanced emotion detection even in sarcastic or ambiguous texts. The novelty lies in addressing class imbalance systematically with SMOTE and leveraging GRU-BiLSTM's complementary strengths, yielding superior performance metrics. This approach contributes to advancing emotion detection tasks, especially in conflict-related social media data, by offering a robust, context-sensitive, and balanced classification method.
Comparison of Machine Learning Algorithm Models in Bitcoin Price Sentiment Analysis Afrinanda, Rizky; Tawa Bagus, Wahyu; Efrizoni, Lusiana
The Indonesian Journal of Computer Science Vol. 12 No. 2 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i2.3180

Abstract

Bitcoin is one of the digital payments that is currently booming, fast delivery makes bitcoin in great demand by many people, currently there are many digital currency exchanges that can be used, one of the well-known ones in Indonesia, namely Indodax. Indodax is a cryptocurrency exchange, not only an exchange, Indodax also provides a chat room containing investors' opinions. Opinions contained in the Indodax chat room can be used to determine whether comments are positive, neutral or negative, so that it can be an investor's decision to sell or buy bitcoin using sentiment analysis. The sentiment analysis process begins with collecting data using an instant data scraper on the Indodax website, data preprcoessing, labeling using vader lexicon, TF-IDF as word weighting, data splitting, naïve Bayes algorithm and support vector machine, feature selection xgboost and gradient boosting, model evaluation with confusion matrix, then comparing the results of the two algorithms. Based on the tests that have been carried out, naïve bayes obtained the best accuracy value of 70.7%, naïve bayes combined with XGBoost obtained the best accuracy value of 86.6%, while the Support vector machine obtained the best accuracy 86.1%, support vector machine combined with gradient boosting obtained the best accuracy value of 88%. Based on these results the use of feature selection can increase the accuracy value of the algorithm.
Opinion Mining menggunakan Algoritma Deep Learning untuk Menganalisis Penggunaan Aplikasi Jamsostek Mobile Azhari, Zahra; Efrizoni, Lusiana; Agustin, Wirta; Yanti, Rini
The Indonesian Journal of Computer Science Vol. 12 No. 2 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i2.3185

Abstract

BPJS Ketenagakerjaan berperan dalam menjaga kesejahteraan para pekerja dan buruh melalui program-program pendidikan dan pelatihan yang diberikan, pelayanan menjadi prioritas terhadap pelanggan untuk memberikan kenyamanan. Melalui aplikasi Jamsostek Mobile yang terdapat di google playstore akan diambil komentar-komentar untuk mendapatkan respon pelanggan terhadap aplikasi Jamsostek mobile untuk dilakukan opinion mining. Komentar yang diambil dari google playstore menggunakan bantuan googleplayscraper, sebanyak 3000 komentar berhasil diambil yang kemudian akan dilakukan tahap pembersihan data, pelabelan, pembobotan kata menggunakan word2vec 300 dimensi dan dilanjutkan menggunakan algoritma Long Short Term Memory. Hasil opinion mining menunjukkan dominasi sentimen negatif sebesar 80.58% dan 19.42% positif dengan tingkat akurasi terbaik yang dihasilkan oleh algoritma LSTM sebesar 87.36%. Hasil penelitian ini akan memberikan wawasan yang berguna bagi pengembang aplikasi untuk meningkatkan kualitas pelayanan dan pengalaman pengguna.
Komparasi Algoritma K-Nearest Neighbors dan Naïve Bayes dalam Klasifikasi Penyakit Diabetes Gestasional Ermy Pily, Annisa Khoirala; Oktavianda; Aprilia, Fanesa; Rahmaddeni; Efrizoni, Lusiana
The Indonesian Journal of Computer Science Vol. 13 No. 1 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i1.3714

Abstract

Diabetes merupakan penyakit metabolik dengan gejala hiperglikemia akibat gangguan sekresi insulin dan aksi insulin. Diabetes gestasional adalah gangguan toleransi glukosa pada wanita hamil. Saat kehamilan, plasenta menghasilkan hormon baru seperti human placental lactogen (HPL), hormon estrogen, dan hormon peningkat resistensi insulin. Gejala diabetes gestasional tidak selalu mudah dikenali, dan seringkali penderitanya mengalami gejala awal secara tidak sadar. Penelitian ini bertujuan untuk membandingkan performa dua algoritma yaitu K-NN dan Naïve Bayes dengan Feature Selection dalam mengklasifikasikan penderita diabetes gestasional. Hasil error terendah dari feature selection dengan iterasi K=4, memperoleh MAE 0.317, MSE 0.142, dan RMSE 0.377. Hasil akurasi pada model KNN dengan K=5 , tanpa Feature Selection sebesar 80% dan K-NN dengan Feature Selection sebesar 77%. Sementara itu, Naïve Bayes tanpa Feature Selection sebesar 77% dan Naïve Bayes dengan Feature Selection sebesar 80%. Dari hasil tersebut K-NN tanpa Feature Selection dan Naïve Bayes dengan Feature Selection mendapatkan hasil yang lebih baik.
Improving Evaluation Metrics for Text Summarization: A Comparative Study and Proposal of a Novel Metric Junadhi, Junadhi; Agustin, Agustin; Efrizoni, Lusiana; Okmayura, Finanta; Habibie, Dedi Rahman; Muslim, Muslim
Journal of Applied Data Sciences Vol 6, No 2: MAY 2025
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v6i2.547

Abstract

This research evaluates and compares the effectiveness of various evaluation metrics in text summarization, focusing on the development of a new metric that holistically measures summary quality. Commonly used metrics, including ROUGE, BLEU, METEOR, and BERTScore, were tested on three datasets: CNN/DailyMail, XSum, and PubMed. The analysis revealed that while ROUGE achieved an average score of 0.65, it struggled to capture semantic nuances, particularly for abstractive summarization models. In contrast, BERTScore, which incorporates semantic representation, performed better with an average score of 0.75. To address these limitations, we developed the Proposed Metric, which combines semantic similarity, n-gram overlap, and sentence fluency. The Proposed Metric achieved an average score of 0.78 across datasets, surpassing conventional metrics by providing more accurate assessments of summary quality. This research contributes a novel approach to text summarization evaluation by integrating semantic and structural aspects into a single metric. The findings highlight the Proposed Metric's ability to capture contextual coherence and semantic alignment, making it suitable for real-world applications such as news summarization and medical research. These results emphasize the importance of developing holistic metrics for better evaluation of text summarization models.
Analisis Performa Penjualan dan Prediksi Omzet dengan Pendekatan Market Basket Analysis Berbasis Data Analytics Ramadhani, Jilang; Efrizoni, Lusiana; Yenni, Helda; Zoromi, Fransiskus
The Indonesian Journal of Computer Science Vol. 14 No. 2 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i2.4788

Abstract

Pesatnya perkembangan bisnis ritel menuntut strategi pemasaran berbasis data untuk meningkatkan performa penjualan dan omzet. Penelitian ini menggunakan Market Basket Analysis (MBA) dengan algoritma Apriori untuk mengidentifikasi pola pembelian konsumen dan Regresi Linear Sederhana untuk memprediksi omzet berdasarkan jumlah transaksi harian. Data transaksi Alfamart Wingky Mart periode Maret–September 2024 dianalisis guna menemukan hubungan antar produk serta tren penjualan. Hasil MBA menunjukkan kombinasi produk Bimoli, Gula, dan Tepung memiliki support 42.16% dan confidence 99.37%, yang dapat dimanfaatkan untuk strategi pemasaran. Model regresi menghasilkan R² sebesar 35.65%, menunjukkan hubungan antara jumlah transaksi dan omzet, meskipun masih terdapat faktor lain yang berpengaruh. Penelitian ini memberikan wawasan strategis bagi bisnis ritel dalam optimasi tata letak produk, promosi bundling, serta peningkatan omzet berbasis analisis data.
Optimization of Content Recommendation System Based on User Preferences Using Neural Collaborative Filtering Lusiana Efrizoni; Junadhi Junadhi; Agustin Agustin
MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer Vol. 24 No. 2 (2025)
Publisher : LPPM Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/matrik.v24i2.4775

Abstract

Recommender systems play a crucial role in enhancing user experience across various digital platforms by delivering relevant and personalized content. However, many recommender systems still face challenges in providing accurate recommendations, especially in cold-start situations and when user data is limited. This study aims to address these issues by optimizing content recommendation systems using Neural Collaborative Filtering (NCF), a deep learning-based approach capable of capturing non-linear relationships between users and items. We compare the performance of NCF with traditional methods such as Matrix Factorization (MF) and Content-Based Filtering (CBF) using the MovieLens-1M dataset. The research method employed is a quantitative approach that encompasses several stages, including preprocessing, model training, and evaluation using metrics such as Root Mean Squared Error (RMSE) and Precision@K. The results of this research are significant, demonstrating that NCF achieves the lowest RMSE of 0.870, outperforming MF with an RMSE of 0.950 and CBF with an RMSE of 1.020. Additionally, the Precision@K achieved by NCF is 0.73, indicating the model’s superior ability to provide more relevant recommendations compared to baseline methods. Hyperparameter tuning reveals that the optimal combination includes an embedding size of 16, three hidden layers, and a learning rate of 0.005. Despite its excellent performance, NCF still faces challenges in handling cold-start cases and requires significant computational resources. To address these challenges, integrating additional metadata and exploring regularization techniques such as dropout are recommended to enhance generalization. The implications of the findings from this study suggest that NCF can significantly improve prediction accuracy and recommendation relevance, thus having the potential for widespread application across various domains, such as e-commerce, streaming services, and education, to enhance user experience and the efficiency of recommendation systems. Further research is needed to explore innovative solutions to address cold-start challenges and reduce computational demands.
Optimalisasi Pengelompokan Gangguan Kecemasan dalam Mendukung Tujuan Pembangunan Berkelanjutan Menggunakan Algoritma K-Means dan K-Medoids Aulia, Rahma; Julianti, Nadea; Putri, Siti Faradila; Efrizoni, Lusiana; Deni, Rahmad
JATISI (Jurnal Teknik Informatika dan Sistem Informasi) Vol 12 No 2 (2025): JATISI (Jurnal Teknik Informatika dan Sistem Informasi)
Publisher : Universitas Multi Data Palembang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35957/jatisi.v12i2.11495

Abstract

Abstract Data clustering is a data mining technique that aims to find hidden patterns in a dataset. The dataset used in this study was taken from the Kaggle public dataset on anxiety attacks. Anxiety disorder is a mental condition characterized by excessive and prolonged feelings of anxiety. Clustering anxiety disorders facilitates finding the cause, effect, and better treatment. Therefore, this study aims to group anxiety disorders using the K-Means and K-Medoids algorithms by considering attributes such as stress level, sleep patterns, and physical activity. The performance of the model is evaluated using the Davies-Bouldin Index (DBI). The results showed that the K-Means algorithm produced the lowest DBI value in cluster ten with an accuracy value of 2.331. This shows that the K-Means algorithm is able to identify significant patterns in anxiety disorder data. This study can be a recommendation for health professionals in making more precise diagnoses, understanding the characteristics of the causes of anxiety disorders. In addition, this study also supports the achievement of the Sustainable Development Goals in an effort to improve the overall health and welfare of the community. Keywords— K-Means, K-Medoids, Anxiety Disorders, Sustainable Development Go als
Co-Authors -, Dwi Haryono Afrinanda, Rizky Agung Marinda Agus Tri Nurhuda Agustin Agustin Agustin Agustin Agustin Agustin, Endy Wulan Ahmad - Fauzan Ahmad Fauzan Ahmad Rizali Anam, M Khairul Andhika, Imam Anthony Anggrawan Anugraha, Yoga Safitra Aprilia, Fanesa Arifin, Muhammad Amirul Armoogum , Sheeba Aulia, Rahma Azhari, Zahra Cikita, Putri Dadynata, Eric Dea Safitri Deni, Rahmad Devi Puspita Sari, Devi Puspita Dhini Septhya Djamalilleil, Said Azka Fauzan Edwar Ali Erlinda, Susi Ermy Pily, Annisa Khoirala ester nababan Fadly Fadly Farhan Pratama Farida Try Puspa Siregar Fatdha, Eiva Febrianda Putra Filza Izzati Finanta Okmayura Firdaus, Muhammad Bambang Firman, Muhammad Aditya Fransiskus Zoromi Fransiskus Zoromi, Fransiskus Habibie, Dedi Rahman Hadi Asnal, Hadi Hafsah Fulaila Tahiyat Handayani, Nadya Satya Haviluddin Haviluddin Helda Yenni, Helda Hidaya Spitri Iftar Ramadhan Ihsan, Raja Muhammad Ike Yunia Pasa Irwanda Syahputra Julianti, Nadea Junadhi Junadhi Junadhi Junadhi Junadhi, Junadhi Karpen Kartina Diah K. W. Kharisma Rahayu Koko Harianto Lathifah, Lathifah Lestari, Fika Ayu Lili Marlia M. Azzuhri Dinata M. Irpan Marhadi, Nanda Maulana, Fitra Melva Suryani Muhammad Bambang Firdaus Muhammad Oase Ansharullah Muhammad Syaifullah MUHAMMAD TAJUDDIN Munawir Munawir Muslim Muslim Nanda, Annisa Nasution , Zikri Hardyan Nurul fadillah, Nurul Oktavianda Oktavianda, Oktavianda Purnama, Muhammad Adji Putra, Febrianda Putri, Adinda Dwi Putri, Siti Faradila R. Guntur Surya Yuwana - Rahmaddeni Rahmaddeni Rahmaddeni, - Rahmiati Rahmiati Rais Amin Raja Muhammad Ihsan Ramadhani, Jilang Rati Rahmadani Ratna Andini Husen Revaldo, Bagus Tri Riadhil Jannah Rini Yanti, Rini Risky Harahap Risman Risman Rizki Astuti Rometdo Muzawi, Rometdo Sahelvi, Elza Salsabila Rabbani Sapina, Nur Sapitri, Riska Mela Sari, Atalya Kurnia Sarjon Defit Sarjon Defit Setiawan , Andri Shahreen Kasim, Shahreen Sholekhah, Fitriana Sularno Supian, Acuan Susandri, Susandri Susanti, Susanti Susi Erlinda Syahrul Imardi Syarifuddin Elmi Tahiyat, Hafsah Fulaila Tashid Tawa Bagus, Wahyu Torkis Nasution Tri Putri Lestari, Tri Putri Tri Revaldo, Bagus Triyani Arita Fitri Ulfa, Arvan Izzatul Unang Rio Uthami, Kurnia Vindi Fitria Wirta Agustin Wirta Wirta Yanti, Rini Yoyon Efendi Yulli Zulianda Zahra Azhari Zakaria , Mohd Zaki Zakaria, Mohd Zaki Zega, Wilman Zikri Hadryan nst Zulafwan Zuriatul Khairi Zuriatul Khairi