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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) Information System Journal (INFOS) Jurnal Computer Science and Information Technology (CoSciTech) KLIK: Kajian Ilmiah Informatika dan Komputer Malcom: Indonesian Journal of Machine Learning and Computer Science Scientific Journal of Informatics Jurnal Riset Pendidikan dan Karakter INOVTEK Polbeng - Seri Informatika
Fitri Insani
Bogor Agricultural University
Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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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.
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

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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
Penerapan Algortitma C4.5 untuk Klasifikasi Sentimen Masyarakat terhadap #RUUKUHP pada Twitter Vusuvangat, Imam; Kurnia Gusti, Siska; Syafira, Fadhilah; Novriyanto, Novriyanto; Insani, Fitri
Jurnal Teknologi Sistem Informasi dan Aplikasi Vol. 6 No. 4 (2023): Jurnal Teknologi Sistem Informasi dan Aplikasi
Publisher : Program Studi Teknik Informatika Universitas Pamulang

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Abstract

Social media, especially Twitter, has developed into an important tool for people to share their opinions and feelings widely. Users often use hashtags to share messages related to certain topics. Some of the issues that lead to the need for sentiment analysis of the Draft Criminal Code are social impact, Public disapproval, Potential legal uncertainty, Potential abuse, Support and criticism. By conducting a sentiment analysis of the draft Penal Code, the government and policymakers can better understand the views of the public, identify possible problems and address them, and make necessary improvements or clarifications to the draft law. This can help ensure that the draft Penal Code has greater public support and adheres to good legal principles. The classification of public responses to this hashtag provides a significant snapshot of public attitudes and perspectives. This study aims to classify public sentiment towards the RUUKUHP hashtag on the Twitter platform using the C4.5 algorithm. This study uses a collection of tweets with the hashtag RUUKUHP which are manually categorized into two and three sentiment categories, namely positive, negative and positive, negative and neutral. In this study, data preprocessing is carried out before training the model which includes removing links, special characters, removing stopwords, and word tokenization. Furthermore, this research uses text representation methods such as TF-IDF to extract features from the tweet text and convert them into numerical vectors used by the C4.5 algorithm. After training the classification model using the C4.5 algorithm with the classified dataset, it evaluates the performance of the model with the metrics of accuracy, recall, precision, and F1 score. Experimental results using 2 categories of Negative and Positive show that the model applied with the C4.5 algorithm achieved an accuracy of 96.6% with a recall of 96.6%, a percision of 97.1% and an F1 score of 96.8. And experiments using 3 categories of Negative, Positive and Neutral achieved an accuracy of 67%, a recall of 67%, a precision of 65%, and an F1 score of 66%. Thus it can be concluded that the results of the RUUKUHP hashtag sentiment classification with 2 class predictions are more relevant than 3 sentiment class predictions with a value reaching 96.6%.
Pengelompokan Tingkat Stres Akademik Pada Mahasiswa Menggunakan Algoritma K-Medoids Nurfadilah, Nova Siska; Budianita, Elvia; Nazir, Alwis; Insani, Fitri; Susanti, Reni
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7409

Abstract

Academic stress is one of the common problems issues by university students due to heavy with heavy workloads, grade pressure, and various academic This condition can have a negatively impact on mental health, productivity and overall academic performance. In the long term, unmaged stress may lead serious psychological disorders. Therefore, it is important to accurately identify and classify the levels of academic stress. This study aims to cluster students’ academic stress levels by utilizing the K-Medoids algorithm. The data analyzed in the research were collected through questionnaires that were filled out by 507 students from the 2021-2023 cohorts, based on a modified version of the Perception of Academic Stress Scale (PASS). The results show that the K-medoids algorithm successfully clustered the data in 2 groups: cluster 0, which represents a moderate stress level with 212 students, and cluster 1, which indicates a high stress level with 295 students. This high-stress cluster exhibited higher average cores on questions 12 and 13 (score 3-5), which fall under the favorable category and are suspected to be the main triggers of academic stress among students in this group. Based on two evalutation metrics-Silhouette Coeficient and Davies-Bouldin Index (DBI)-it can be concluded that the optimal number of clusters for this data set is K=2. However, the clustering separation was not optimal due to he variation in study programs and the uneven distribution of respondets across academic years. This research is expected to provide direction the development intervation policies and strategies to support student welfare.
Penerapan Algoritma K-Means Untuk Mengelompokkan Tingkat Stres Akademik Pada Mahasiswa Wiranti, Lusi Diah; Budianita, Elvia; Nazir, Alwis; Insani, Fitri; Susanti, Reni
Building of Informatics, Technology and Science (BITS) Vol 7 No 1 (2025): June (2025)
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i1.7410

Abstract

Academic stress is a prevalent concern among university students, often arising from various challenges within the academic environment. These challenges may include tight assignment deadlines, elevated expectations from both lecturers and parents, ineffective time management, and negative self-assessment. If left unaddressed, such stress can negatively impact students’ academic performance and mental well-being. This study focuses on categorizing student academic stress levels using the K-Means clustering algorithm. Data were collected from 507 participants through a customized version of the Perception of Academic Stress Scale (PASS) questionnaire, adapted to suit the study context. Prior to analysis, the data were preprocessed and converted into a numerical format. Clustering was performed using Python on the Google Colab platform. To assess the clustering performance, two evaluation metrics were used: the Davies-Bouldin Index (DBI) and the Silhouette Coefficient. Lower DBI values suggest that the clusters formed are more compact and distinct from each other, while higher Silhouette values indicate better clustering performance. From the evaluation, the best clustering result was found when the number of clusters was 2, with a DBI score of 1.43 and a Silhouette score of 0.27. Nonetheless, these values still fall short of the ideal range, likely due to the heterogeneous nature of the data, as participants came from five different departments within the Faculty of Science and Technology. Moreover, the number of responses varied across academic years (2021–2023). Cluster 1 comprised 229 students identified as having low levels of academic stress, as shown by their lower questionnaire scores. In contrast, Cluster 2 consisted of 278 students with higher levels of stress, as reflected in their higher scores (ranging from 3 to 5) on positively worded items.
Pengembangan Sistem Klasifikasi Sentimen Berita PTPN IV Regional III Berbasis Web Menggunakan IndoBERT: Development of a Web-Based News Sentiment Classification System for PTPN IV Regional III Using IndoBERT Wahyudi, Diki; Insani, Fitri; Jasril, Jasril; Yusra, Yusra
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2825

Abstract

Aktivitas pemantauan berita di Humas PTPN IV Regional III masih dilakukan secara manual sehingga memerlukan waktu yang lama, rentan terhadap subjektivitas, dan berisiko melewatkan informasi penting. Penelitian ini bertujuan untuk merancang dan membangun sistem pemantauan berita berbasis web yang mengintegrasikan model Indonesian Bidirectional Encoder Representations from Transformers (IndoBERT) untuk klasifikasi sentimen secara otomatis. IndoBERT dipilih karena dilatih khusus menggunakan korpus berbahasa Indonesia sehingga lebih mampu memahami karakteristik bahasa dan sentimen dibandingkan model multilingual. Dataset yang digunakan terdiri atas 1.200 berita dengan distribusi seimbang pada tiga kelas sentimen: positif, netral, dan negatif. Model dikembangkan menggunakan metode fine-tuning dan menghasilkan akurasi 75% serta macro F1-score 0,74. Kelas negatif memperoleh kinerja terbaik dengan F1-score 0,85, sedangkan kelas netral mencapai 0,63 akibat karakteristik sentimen yang ambigu. Sistem ini dikembangkan menggunakan Flask, Next.js, MySQL, dan SerpAPI. Hasil pengujian Black Box menunjukkan bahwa seluruh fitur berfungsi sesuai kebutuhan, sedangkan User Acceptance Testing (UAT) memperoleh tingkat penerimaan sebesar 96,5%. Penelitian ini berkontribusi melalui implementasi sistem pemantauan berita korporasi berbasis web yang mengintegrasikan IndoBERT secara end-to-end.
Co-Authors Abdul Aziz Afriyanti, Liza Ahmad Fauzan Al Fiqri, M. Faiz Ananda, Rahmadan Alam Ardan Baehaqi Damayanti, Elok Darlianti, Sri Indah Elin Haerani Elvia Budianita Fachry Abda El Rahman Fadhilah Syafria Fakhira, Adzra Febi Yanto Fitria Novitasari Gusti, Siska Kurnia Hasibuan, Aldiansyah Pramudia Iis Afrianty Jasril Jasril Jasril Jasril 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 WAHYUDI, DIKI Wiranti, Lusi Diah Yusra Yusra, Yusra