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All Journal ComEngApp : Computer Engineering and Applications Journal Jurnal Online Mahasiswa (JOM) Bidang Ilmu Sosial dan Ilmu Politik Jurnal CoreIT INOVTEK Polbeng - Seri Informatika Jurnal Teknologi Sistem Informasi dan Aplikasi Building of Informatics, Technology and Science Zonasi: Jurnal Sistem Informasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) Information System Journal (INFOS) Jurnal Computer Science and Information Technology (CoSciTech) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer Bulletin of Information Technology (BIT) Malcom: Indonesian Journal of Machine Learning and Computer Science Indonesian Journal of Innovation Multidisipliner Research Scientific Journal of Informatics Jurnal Riset Pendidikan dan Karakter Indonesian Journal of Innovation Multidisipliner Research INOVTEK Polbeng - Seri Informatika
Fitri Insani
Bogor Agricultural University
Religion Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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SMS Phishing Detection Model with Hyperparameter Optimization in Machine Learning Abdillah, Rahmad; Insani, Fitri
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 11, No 1 (2025): June 2025
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v11i1.35547

Abstract

Phishing is one of the growing cybersecurity threats, including through SMS, known as smishing. This research aims to build a model for SMS phishing detection using a machine learning approach optimized through hyperparameter tuning techniques. The data used is obtained from personal SMS messages collected through questionnaires, which are then labeled by information security experts. The SMS text is cleaned using Natural Language Processing (NLP) techniques and represented using the TF-IDF method. Ten classification algorithms are tested in this study: K-NN, Logistic Regression, Support Vector Machine (SVM), Decision Tree, Random Forest, AdaBoost, Bagging, ExtraTree, Gradient Boosting, and XGBoost. Hyperparameter optimization is performed using Grid Search and Optuna, and performance is evaluated using accuracy, F1-score, and ROC-AUC metrics. The results show that the SVM and Logistic Regression models performed the best, achieving accuracy up to 98.5%. Hyperparameter optimization techniques have proven effective in improving the performance of SMS phishing classification models. This research is expected to contribute to the development of accurate and efficient SMS phishing detection systems.
Penggunaan Convolutional Neural Network NASNetLarge Dalam Klasifikasi Citra Daging Babi dan Sapi Aqilah, M Alfandri; Jasril; Sanjaya, Suwanto; Insani, Fitri
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.666

Abstract

The adulteration of beef with pork is a serious issue in Indonesia, particularly for Muslim consumers who are required to consume halal products. According to a Kompas (2020) report, a case of meat adulteration involving 100 kilograms of mixed meat sold as beef was discovered in Tangerang City. This practice not only violates religious laws but also poses threats to public health and consumer trust. To address this challenge, this study adopts a deep learning approach using NASNetLarge for the classification of pork, beef, and mixed meat images. Unlike previous research that utilized EfficientNet-B2 and achieved an accuracy of 98.23%, this study’s NASNetLarge approach produced a comparably competitive accuracy of 98.03%. The dataset used consists of 1,932 images sourced from the Kaggle platform, which were processed through preprocessing and augmentation stages. The data were then split into two distribution scenarios: the entire dataset and a balanced class dataset with 90:10 and 80:20 ratios. Evaluation results show that the best parameter combination was achieved in the first scenario with a 90:10 ratio using augmented images, a learning rate of 0.001, 128 dense units, and the Adam optimizer. The model achieved the highest accuracy of 98.03%, with a precision of 98.63%, recall of 98.40%, and an F1-score of 98.50%. These results indicate that NASNetLarge is effective in accurately and consistently classifying meat images. Image augmentation significantly improved model performance, and the 90:10 data ratio yielded more optimal results compared to 80:20. These findings have the potential to support food surveillance efforts by enabling rapid and accurate detection of meat adulteration.
Application of ARIMA and ARIMAX Methods to Predict the Number of Visitors to Hotel XYZ Pekanbaru Vernando, Julio; Insani, Fitri; Okfalisa, Okfalisa; Kurnia, Fitra
INOVTEK Polbeng - Seri Informatika Vol. 10 No. 2 (2025): July
Publisher : P3M Politeknik Negeri Bengkalis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35314/enrfna19

Abstract

Predicting the number of visitors to Hotel XYZ is one of the steps that can be taken by the hotel to find out how many visitors will increase in each upcoming holiday season. The purpose of this study is to forecast the number of visitors to Hotel XYZ from June 2023 to July 2024 using the ARIMA and ARIMAX comparison methods. The research methodology encompasses problem identification, data collection, data processing, and ARIMA and ARIMAX analysis, which involves testing the parameters (p, d, q) selected based on the ACF and PACF using the AIC Model. Based on the test results, ARIMAX (5, 0, 3) has the lowest AIC, which is 3495.2, followed by ARIMAX (3, 0, 5), which has a slightly higher AIC. The results showed that the ARIMAX (5, 0, 3) model is the most accurate model for predicting data (eg the number of hotel guests, room demand, or income), with an RMSE value of 15.80% and a MAPE of 18.90%. Therefore, research that applies the ARIMAX model can provide real benefits in supporting operational efficiency, resource management, and hotel business strategy, ultimately increasing the competitiveness and profitability of the hotel.
PENGELOMPOKAN DATA KONDISI MESIN SCREW PRESS MENGGUNAKAN ALGORITMA FUZZY C-MEANS Jasril, Jasril; Al Fiqri, M. Faiz; Sanjaya, Suwanto; Handayani, Lestari; Insani, Fitri
Information System Journal Vol. 8 No. 01 (2025): Information System Journal (INFOS)
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/infosjournal.2025v8i01.2133

Abstract

Kinerja mesin screw press sangat memengaruhi efisiensi dan kualitas produksi minyak kelapa sawit. Salah satu komponen penting dalam sistem ini adalah Back Pressure Vessel (BPV) yang menyalurkan uap ke berbagai stasiun proses. Penelitian ini bertujuan untuk mengelompokkan kondisi mesin berdasarkan temperatur dan tekanan menggunakan algoritma Fuzzy C-Means (FCM). Data yang dianalisis berasal dari mesin BPV PT. XYZ periode April–Mei 2024 sebanyak 23.002 entri. Tahapan penelitian meliputi seleksi data, pra-pemrosesan, normalisasi Min-Max Scaler, klasterisasi FCM, dan evaluasi menggunakan metode Elbow dan Davies-Bouldin Index (DBI). Hasil awal menunjukkan tiga klaster dengan distribusi kondisi mesin dari stabil hingga memerlukan perawatan. Metode Elbow menunjukkan jumlah klaster optimal sebanyak empat, sedangkan DBI menunjukkan dua klaster dengan nilai terbaik 0,389. Hasil ini menunjukkan bahwa FCM mampu mengelompokkan kondisi mesin secara efektif dan dapat digunakan sebagai dasar dalam pengambilan keputusan perawatan. Penelitian ini disarankan untuk dikembangkan dengan atribut tambahan.
Penerapan Algoritma K-Means Clustering pada Kinerja Mesin Screw press Kurnia Rahman, Fikri; Jasril; Sanjaya, Suwanto; Handayani, Lestari; Insani, Fitri
Bulletin of Information Technology (BIT) Vol 6 No 2: Juni 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bit.v6i2.2002

Abstract

The screw press is one of the machines used in the process of separating oil from tanks containing Fresh Fruit Bunches (FFB). The machine consists of a twin-screw system that functions to extract oil from the pressing unit, with back pressure applied by a hydraulic double cone. The mixed fruit residue is compreWCSSd, causing the oil contained within the residue to be released due to the pressure exerted by the press machine. Maintenance and repair of machinery are eWCSSntial activities to support productive operations in any sector. Therefore, it is necessary to conduct analysis to identify patterns in machine conditions within the factory. One effective approach to discovering machine condition patterns is through clustering techniques. Clustering is a method of grouping data based on certain parameters to form clusters of objects that share similar characteristics. In this study, data were collected from PT. XYZ for the period of April 2024 to May 2024, with a total of 23,002 records. The analysis was conducted using the K-Means Clustering algorithm, with testing carried out on 3 to 15 clusters. Based on the evaluation using the Davies-Bouldin Index (DBI), the most optimal clustering result was obtained with 3 clusters, achieving the lowest DBI value of 0.386. Meanwhile, using the Elbow Method, the optimal number of clusters was determined to be 4, as indicated by the Elbow point on the WCSS graph, with a Sum of Square Error (WCSS) value of 270. Therefore, it can be concluded that the clustering results using the K-Means Clustering algorithm are relevant for identifying machine condition patterns and are expected to assist in monitoring and managing the condition of the screw press machine.
Turbofan Engine Remaining Useful Life Prediction Using 1-Dimentional Convolutional Neural Network Fauzan, Ahmad; Handayani, Lestari; Insani, Fitri; Jasril; Sanjaya, Suwanto
Computer Engineering and Applications Journal (ComEngApp) Vol. 13 No. 3 (2024)
Publisher : Universitas Sriwijaya

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

Abstract

Turbofan engines have been the dominant type of engine in aircraft for the last forty years. Ensuring the quality of these engines is crucial for flight safety, particularly for long-distance flights. However, their performance degrades over time, impacting flight safety. To address this issue, it is essential to predict potential engine failures by estimating the Remaining Useful Life (RUL) of the engines Deep learning, especially Convolutional Neural Networks (CNNs), has demonstrated exceptional proficiency in handling intricate, non-linear data, leading to improved RUL predictionsdue to their ability to process complex and non-linear data. In this project, a 1-D CNN is used to predict RUL using the NASA C-MAPSS FD001 dataset, which consists of 3 settings and 21 sensors, though sensors with stagnant readings are excluded. The dataset is normalized using min-max and z-score methods, and then segmented into sequences for input into the 1-D CNN model. Various training scenarios were evaluated, with the best RMSE of 3.26 achieved using 10 epochs, a learning rate of 0.0001, and z-score normalization. The results indicate that feature selection can produce a lower RMSE compared to scenarios without feature selection.
Comparative Analysis: Accuracy of Certainty Factor and Dempster Shafer Methods in Expert Systems for Tropical Disease Diagnosis Yanti, Novi; Insani, Fitri; Okfalisa, Okfalisa; Zain, Ruri Hartika; Setiawan, Adil
Scientific Journal of Informatics Vol. 12 No. 3: August 2025
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v12i3.28047

Abstract

Purpose: This study aims to diagnose Neglected Tropical Diseases early by applying the concept of an expert system as a tool that works by mimicking the thought patterns of an expert (doctor). The methods applied in this expert system are Certainty Factor and Dempster Shafer. Both methods work by combining a number of pieces of evidence (symptoms) to produce a confidence value for a disease. Methods: The study began with discussions and interviews with experts to collect information and data about Neglected Tropical Diseases. Conducting a literature review study to enrich knowledge about Neglected Tropical Diseases. Two main inference methods are used to detect diseases based on patient symptoms. The Certainty Factor method uses expert value weighting parameters and patient input value weighting as a basis for knowledge. The Dempster Shafer method only uses expert value weighting in analyzing the probability of symptoms to produce a level of diagnostic accuracy. Result: The Certainty Factor method works by integrating patient and expert weight values into its calculations. Meanwhile, the Dempster Shafer method considers expert weight values without involving patient weight values. Expert system searches using the Forward Chaining inference engine show that the Certainty Factor method has an accuracy probability value of up to 90%. Meanwhile, the Dempster Shafer method has an accuracy value of 70%. Novelty: The results of the study show that expert systems can be applied in the health sector, especially in diagnosing Neglected Tropical Diseases. Of the two methods used, the Certainty Factor method shows a high accuracy value, so it can help detect Neglected Tropical Diseases early and provide treatment solutions to improve health.
PERBANDINGAN METODE K-NEAREST NEIGHBOR DAN MODIFIED K-NEAREST NEIGHBOR UNTUK KLASIFIKASI KELUARGA BERESIKO STUNTING Pratama, Dandi Irwayunda; Insani, Fitri; Yanto, Febi; Afrianty, Iis
ZONAsi: Jurnal Sistem Informasi Vol. 7 No. 1 (2025): Publikasi artikel ZONAsi: Jurnal Sistem Informasi Periode Januari 2025
Publisher : Universitas Lancang Kuning

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31849/zn.v7i1.24698

Abstract

Stunting disebabkan oleh kekurangan gizi kronis, yang menghambat pertumbuhan terhambat pada anak dan dapat memengaruhi kesehatan jangka panjang. Penelitian ini bertujuan untuk mengklasifikasikan keluarga beresiko stunting menggunakan metode K-Nearest Neighbor (K-NN) dan Modified K-Nearest Neigbor (MK-NN). Perbandingan keduanya dilakukan dengan tujuan memberikan gambaran lebih jelas mengenai metode mana yang lebih cocok dalam membantu dalam memilih algoritma yang memberikan hasil yang optimal. Data yang digunakan terdiri dari 23607 data keluarga dan 20 parameter, diperoleh dari Balai Penyuluhan KB (Kampung Berencana) di Kecamatan Tuah Madani. Hasil menunjukkan bahwa MK-NN memberikan performa lebih konsisten pada berbagai nilai k dengan akurasi mencapai 99.28% terutama pada rasio 80:20 dan 70:30. Sebaliknya, K-NN mencapai akurasi maksimum 99.36% tetapi mengalami fluktuasi pada nilai k tertentu. MK-NN juga unggul dalam metrik precisision, recall dan f1-score menunjukkan mampu menghadapi data yang kompleks. Dapat disimpulkan bahwa MK-NN lebih efektif dan stabil dibandingkan K-NN. Penelitian ini menyarankan penggunaan data ekonomi seperti pendapatan dan pekerjaan orang tua pada studi mendatang untuk memberikan hasil klasifikasi yang lebih menyeluruh dan akjrat dalam mendukung kebijakan stunting.
PENGEMBANGAN MEDIA POP UP BOOK PADA PEMBELAJARAN TEMATIK TERPADU KELAS IV SEKOLAH DASAR Rossa, Rona; Insani, Fitri
Jurnal Riset Pendidikan Dasar Dan Karakter Vol 6 No 1 (2024): JURNAL RISET PENDIDIKAN DASAR DAN KARAKTER
Publisher : LP2M Universitas Adzkia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59701/pdk.v6i1.229

Abstract

This study was motivated by issues such as students’ concentration being focused mainly at the beginning of lessons and the lack of creative and innovative learning media. Existing media at school were still simple and failed to attract students' interest. To address this, a pop-up book was developed—paper-based media that displays three-dimensional objects when opened. This media was designed for the theme “Caring for Living Beings” in Grade IV of elementary school. The research employed the 4-D development model (Define, Design, Development, Disseminate). Validation results showed that the media was highly valid in terms of design (94.2%) and content (95.4%), and fairly valid in language (77%). Practicality tests yielded 98% from teachers and 85.4% from students, categorized as very practical and practical, respectively. The effectiveness test scored 87%, indicating high effectiveness. Thus, the pop-up book media is considered very valid, practical, and effective for use in integrated thematic learning
Implementasi Algoritma K-Means dalam Menentukan Clustering pada Penilaian Kepuasan Pelanggan di Badan Pelatihan Kesehatan Pekanbaru Fahrozi, Aqshol Al; Insani, Fitri; Budianita, Elvia; Afrianty, Iis
Indonesian Journal of Innovation Multidisipliner Research Vol. 1 No. 4 (2023): December
Publisher : Institute of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/ijim.v1i4.53

Abstract

This research discusses the implementation of the K-Means algorithm in determining clustering in customer satisfaction assessments at the Pekanbaru Health Training Agency. Customer satisfaction is the level of a person's feelings to perceive the comparison between the consumer's impression of the level of product and service performance and the customer's or buyer's expectations. The aim of this research is to see the level of customer satisfaction with the Pekanbaru Health Training Agency (Bapalkes) services using K-means clustering and how high the level of customer satisfaction is using the K-means Clustering method. In this research, the data used is Health Training Center customer data from 2019 and 2023. Data was collected through questionnaires distributed via Google form. Creating a rule model for the collected data using the k-means algorithm and rapidminer software. From the research results obtained using the K-Means algorithm in clustering customer data, it can provide customer segmentation results that are in line with expectations, so that the Pekanbaru Health Training Agency can easily understand the characteristics of its customers based on their clusters and their satisfaction. Then, using the elbow and Davies Bouldin methods, we also provide a solution for selecting the right number of clusters so that performance is more optimal and produces more accurate customer segmentation results. From the calculations of the k-means algorithm, it was obtained that the response value was very dominant at 259 who expressed satisfaction and 44 people who expressed dissatisfaction from 303 customers, so that the k-means algorithm used sensitivity and specificity tests, 86% expressed satisfaction and 14% expressed dissatisfaction with services provided by the Pekanbaru Health Training Agency.
Co-Authors Abdul Aziz Afriyanti, Liza Ahmad Fauzan Al Fiqri, M. Faiz Ananda, Rahmadan Alam Ardan Aqilah, M Alfandri Baehaqi Berliana, Trisia Intan Damayanti, Elok Darlianti, Sri Indah Elin Haerani Elvia Budianita Fachry Abda El Rahman Fadhilah Syafria Fahrozi, Aqshol Al Fakhira, Adzra Febi Yanto Fitria Novitasari Gusti, Siska Kurnia Hamwar, Syahbudin Hasibuan, Aldiansyah Pramudia Iis Afrianty Jasril Jasril Jasril Jasril Kurnia Rahman, Fikri kurnia, fitra Lestari Handayani Miftah Farid Muhammad Abdillah Muhammad Affandes Muhammad Fikry Muhammad Irsyad Muhammad Ravil Mulyadi, Syahrul Najmi, Risna Lailatun Nazir, Alwis Nova Yohana Novriyanto Novriyanto Nurfadilah, Nova Siska Okfalisa Okfalisa Oktavia, Lola Pizaini Pizaini Pratama, Dandi Irwayunda Rahmad Abdillah Reni Susanti Reski Mai Candra Rossa, Rona Ruri Hartika Zain Saputra, Nugroho Wahyu Setiawan, Adil Subhi, Yazid Abdullah Surya Agustian Suwanto Sanjaya Syafira, Fadhilah Tri Prastio Nugroho Utari, Roid Fitrah Vernando, Julio Vitriani, Yelfi Vusuvangat, Imam Wiranti, Lusi Diah Yusra Zahri, Firman