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RANCANGAN CASE-BASED REASONING MENGGUNAKAN SORENSON COEFFICIENT Nugraheni, Murien
Jurnal Informatika Vol 6, No 1: Januari 2012
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (23.425 KB)

Penalaran berbasis kasus (Case-Based Reasoning) untuk melakukan diagnosa penyakit berdasarkan gejala-gejala yang ada. Proses diagnosa dilakukan dengan cara memasukkan kasus baru (target case) yang berisi gejala-gejala penyakit yang akan didiagnosa, kemudian dilakukan proses similaritas antara kasus baru dengan kasus-kasus (source case) yang sudah tersimpan di dalam basis data (case-based) sistem. Kasus dengan nilai similaritas tertinggi akan diambil dan kemudian solusi dari kasus tersebut akan dijadikan solusi bagi kasus yang baru. Metode similaritas yang digunakan adalah Sorenson Coefficient. Jika suatu kasus tidak berhasil didiagnosa, maka akan dilakukan revisi kasus oleh pakar. Kasus yang berhasil direvisi akan disimpan untuk dijadikan pengetahuan baru (fresh knowledge).

Implementasi SCRUM Pada Pengenalan Aksara Lampung Menggunakan Augmented Reality Arfiani, Ika; Nugraheni, Murien; Sulistyono, Danang
Building of Informatics, Technology and Science (BITS) Vol 3 No 3 (2021): December 2021
Publisher : Forum Kerjasama Pendidikan Tinggi

Lampung Province has 20 Lampung characters and 12 Lampung scripts as characters that need to be preserved. Lampung script learning is currently still using conventional methods so that many students begin to ignore this subject because it feels less interesting and boring. This study aims to build an application that can help users introduce Lampung script in the world of education on smartphones by applying Augmented Reality technology. By applying Marker Based Tracking which is one of the methods used in the development of Augmented Reality technology. This method works by recognizing and identifying patterns on markers to bring up virtual objects into the real environment. The system development uses the waterfall method with the stages of problem identification, initial planning, design and design, implementation, testing, and evaluation. This results in an Augmented Reality application to introduce Lampung script which is equipped with features showing 3D Lampung script, pronunciation of each Lampung script object, script gallery, guide for each menu and 20 Lampung script markers. Which type of smartphone camera can affect the application's ability to see objects at a certain angle to the marker, but is still quite safe at angles between 500 to 1800.

Classification of Character Types of Wayang Kulit Using Extreme Learning Machine Algorithm Fatmayati, Fryda; Nugraheni, Murien; Nuraini, Rini; Rossi, Farli
Building of Informatics, Technology and Science (BITS) Vol 5 No 1 (2023): June 2023
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v5i1.3568

Wayang Kulit, which is an original Indonesian culture, is conditioned by the meaning of life in every performance. However, Wayang Kulit is currently less popular among young people due to a lack of understanding of the art of Wayang Kulit performance. To be able to provide knowledge to the younger generation about Wayang Kulit, one of which is by introducing the characters that exist in Wayang Kulit performances. This study aims to build an image classification system for Wayang Kulit characters by applying the neural network method using Extreme Learning Machine (ELM) and morphological feature extraction. Morphological feature extraction provides information about the shape characteristics of objects present in the image which are then used for input in the classification process. The Extreme Learning Machine (ELM) method may arbitrarily establish the weight value between the input neurons and the hidden layer during the classification step, resulting in a quicker learning pattern. Based on the test results using the confusion matrix, the accuracy value is calculated to get a value of 81%.

Pengembangan Sistem Informasi Monitoring Dan Evaluasi Dosen Menggunakan Metode Agile Feature Mumtas, Fuad; Nugraheni, Murien
Insect (Informatics and Security): Jurnal Teknik Informatika Vol. 10 No. 1 (2024): Maret 2024
Publisher : Universitas Muhammadiyah Sorong

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33506/insect.v10i1.3648

Tantangan dalam proses monitoring dan evaluasi perkuliahan yang dilakukan secara manual pada Fakultas Teknik seringkali mengalami masalah seperti duplikasi data, dan waktu analisis yang lama. Dengan demikian, penelitian ini bertujuan untuk mengembangkan sebuah sistem informasi untuk monitoring dan evaluasi perkuliahan di Fakultas Teknik Universitas Negeri Jakarta. Pendekatan yang dilakukan dalam penelitian ini yaitu menggunakan metode Agile Feature untuk mempermudah pengisian data monitoring dan evaluasi perkuliahan oleh Penanggung Jawab mata kuliah, serta mempercepat dan mempermudah proses analisis dan kalkulasi kehadiran dosen. Fakultas Teknik. Setelah fitur-fitur pada sistem selesai dikembangkan, berikutnya dilakukan pengujian dengan User Acceptance Test (UAT) untuk mengevaluasi tanggapan pengguna. Data kuantitatif dari UAT digunakan untuk menilai tingkat penerimaan dan kesiapan pengguna dalam menggunakan sistem. Hasil dari penelitian ini yaitu berupa aplikasi berbasis website untuk melakukan Monitoring dan Evaluasi Perkuliahan di Fakultas Teknik Universitas Negeri Jakarta, yang dapat memberikan hasil rekapitulasi dari monitoring dan evaluasi perkuliahan yang terdiri dari monitoring dan evaluasi awal, monitoring dan evaluasi tengah, monitoring dan evaluasi akhir, dan monitoring dan evaluasi satu semester.

Expert System for Diagnosing Learning Disorders in Children Using the Dempster-Shafer Theory Approach Nugraheni, Murien; Nuraini, Rini; Tonggiroh, Mursalim; Nurhayati, Siti
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12960

Learning disorders can occur in children where a child experiences difficulty mastering important skills such as reading, writing, or arithmetic. Learning disorders can have an emotional impact on children, such as low self-confidence, anxiety, or frustration. Therefore, it is important for parents and educators to recognize the signs of learning disorders so that appropriate intervention can be given. The aim of this research is to develop an expert system that can diagnose learning disorders in children using the Dempster-Shafer Theory algorithm to make it easier to diagnose and produce the right diagnosis. The Dempster-Shafer Theory approach has the ability to provide probability values in evidence based on the level of belief and reasoning in accordance with logic and then combine it with information from certain events. This research produces an expert system built on a website that can diagnose based on symptoms and display diagnosis results, definitions of types of learning disorders, and treatment options. The accuracy test results show a value of 92%, which means that the system built using the Dempster-Shafer Theory approach is able to diagnose learning disorders in children well.

Design and Implementation of Sucirata-Based Instrusion Detection System as a Network Security System Cloud Computers Idrus, Ali; Sugiyanta, Lipur; Nugraheni, Murien; Subhiyanto, S
IJISTECH (International Journal of Information System and Technology) Vol 7, No 2 (2023): The August edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i2.310

Cloud computing is currently being developed and widely used by companies that require large and efficient computing resources. As technology evolves, security threats in cloud services continue to increase. Various threats in cloud computing technology can be avoided by maximizing the identification of security holes. Information threats associated with cloud computing require network and service security against possible attacks. Suricata is a threat detection identifier supported by existing rules. When an attack is detected, Suricata will create a log of the attack committed, Suricata can also perform automatic detection at level 7. The author collected the results of the attack in a log. Sign Suricata and the authors also evaluate whether Suricata can detect port scanning, brute force, denial of service, and backdoors for Cloud Computing. From the test results, optimal results were obtained from the results of attacks detected by the Suricata Intrusion Detection System (IDS) logs in the /var/log/suricata/fast directory.log, the author added that the Suricata configuration is not only for detection, so it can also run drops if there is suspicious activity using network filters that already exist in Suricata and manipulated configuration assumptions to optimally improve security in the cloud.

Design and Implementation of Sucirata-Based Instrusion Detection System as a Network Security System Cloud Computers Idrus, Ali; Sugiyanta, Lipur; Nugraheni, Murien; Subhiyanto, S
IJISTECH (International Journal of Information System and Technology) Vol 7, No 2 (2023): The August edition
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v7i2.310

Cloud computing is currently being developed and widely used by companies that require large and efficient computing resources. As technology evolves, security threats in cloud services continue to increase. Various threats in cloud computing technology can be avoided by maximizing the identification of security holes. Information threats associated with cloud computing require network and service security against possible attacks. Suricata is a threat detection identifier supported by existing rules. When an attack is detected, Suricata will create a log of the attack committed, Suricata can also perform automatic detection at level 7. The author collected the results of the attack in a log. Sign Suricata and the authors also evaluate whether Suricata can detect port scanning, brute force, denial of service, and backdoors for Cloud Computing. From the test results, optimal results were obtained from the results of attacks detected by the Suricata Intrusion Detection System (IDS) logs in the /var/log/suricata/fast directory.log, the author added that the Suricata configuration is not only for detection, so it can also run drops if there is suspicious activity using network filters that already exist in Suricata and manipulated configuration assumptions to optimally improve security in the cloud.

Indonesian Fake News Classification Using Transfer Learning in CNN and LSTM Praha, Tohpatti Crippa; Widodo, Widodo; Nugraheni, Murien
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.2.2126

Fake news spreads quickly and is challenging to stop due to the ease of accessing and sharing information online. Deep learning techniques are a method that can be used to identify fake news quickly and accurately. The types of neural networks commonly utilized in deep learning architectures include Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM), which can perform well when managing the task of classifying fake news, according to several pertinent studies. Regarding handling instances of Indonesian fake news classification, this study compares how well the CNN and LSTM models perform. However, given that Indonesian is a low-resource language with scant documentation, it is challenging to build an adequate data set. At the same time, the CNN and LSTM classification models require significant training data. We proposed a transfer learning method by combining two classification models with a pre-trained IndoBERT language model. 1340 news text data were used, including 643 actual news texts from CNN Indonesia, Liputan6, and Detik and 697 fake news texts from TurnBackHoax. As a result, the performance of the combination of the LSTM classification model with IndoBERT outperformed that of the CNN classification model with IndoBERT, which only produced an accuracy of 92.91%, down by 6%, and was able to produce an accuracy of up to 97.76%, an increase of 4.8% from before. Furthermore, the results show that the LSTM classification model outperforms the CNN classification model in capturing the representation created by IndoBERT. Additionally, these insights may serve as a basis for future research on identifying fake news in Indonesia, helping to improve methods for combatting misinformation in Indonesia.

ANALISIS PREDIKSI TUMBUH KEMBANG ANAK DENGAN MACHINE LEARNING Nugraheni, Murien; Widodo, Widodo; Lestari, Uning; Effendy, Vina Ardelia; Yunanto, Prasetyo Wibowo; Amannu, Ramadhan
Infotech: Journal of Technology Information Vol 11, No 1 (2025): JUNI
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i1.389

Stunting is a major chronic nutritional issue that remains a significant challenge in Indonesia. This study aims to predict the risk of stunting in children and enhance prevention efforts by analyzing the health and nutritional status of parents. The research employs Machine Learning methods by comparing the performance of the Decision Tree and Gaussian Naive Bayes algorithms. The dataset was obtained from open data sources and analyzed using Google Colab, with a Technology Readiness Level (TRL) of level 3. Evaluation results show that both algorithms achieved an accuracy of 95.35% based on the confusion matrix. The model accurately identified 2 stunting cases (True Positive) and 41 non-stunting cases (True Negative), indicating a high level of classification reliability. These findings suggest that Machine Learning approaches can be effectively utilized as early detection tools to support stunting prevention strategies in children.

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