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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Reconfigurable and Embedded Systems (IJRES) Jurnal Ilmu Komputer dan Informasi TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics Jurnal Karinov Jurnal Kajian Matematika dan Aplikasinya Prosiding Seminar Nasional Pengabdian Kepada Masyarakat
Denis Eka Cahyani
Universitas Negeri Malang
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Mathematics Public Health Social Sciences Other

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Indonesian part of speech tagging using maximum entropy markov model on Indonesian manually tagged corpus Denis Eka Cahyani; Winda Mustikaningtyas
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 11, No 1: March 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v11.i1.pp336-344

Abstract

This research discusses the development of a part of speech (POS) tagging system to solve the problem of word ambiguity. This paper presents a new method, namely maximum entropy markov model (MEMM) to solve word ambiguity on the Indonesian dataset. A manually labeled “Indonesian manually tagged corpus” was used as data. Furthermore, the corpus is processed using the entropy formula to obtain the weight of the value of the word being searched for, then calculating it into the MEMM Bigram and MEMM Trigram algorithms with the previously obtained rules to determine the part of speech (POS) tag that has the highest probability. The results obtained show POS tagging using the MEMM method has advantages over the methods used previously which used the same data. This paper improves a performance evaluation of research previously. The resulting average accuracy is 83.04% for the MEMM Bigram algorithm and 86.66% for the MEMM Trigram. The MEMM Trigram algorithm is better than the MEMM Bigram algorithm.
An Automatic Approach for Bilingual Tuberculosis Ontology Based on Ontology Design Patterns (ODPs) Bambang Harjito; Denis Eka Cahyani; Afrizal Doewes
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 16, No 1: February 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v16i1.6587

Abstract

Ontology is a representation term used to describe and represent a domain of knowledge. Manually ontology development is currently considered complex, requiring a lot of time and effort. This research was proposed to develop methods to build automatic domain ontology bilingual in Indonesian and English by using corpus and ontology design patterns (ODPs) in tuberculosis disease. In this study, the methods used were to combine ontology learning from text and ontology design patterns to decrease the role of expert knowledge. The methods in this research consist of six stages are term and relation extraction, matching with Tuberculosis glossary, matching with ODPs, score computation similarity term and relations with ODPs, ontology building and ontology evaluation. The results of ontology construction were 362 terms and 44 relations with 260 terms were added. The calculation accuracy of ontology construction was 71%. Ontology construction had higher complexity and shorter time as well as decreases the role of the expert knowledge which proof that the automatic ontology evaluation is better than manual ontology construction.
Performance comparison of TF-IDF and Word2Vec models for emotion text classification Denis Eka Cahyani; Irene Patasik
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3157

Abstract

Emotion is the human feeling when communicating with other humans or reaction to everyday events. Emotion classification is needed to recognize human emotions from text. This study compare the performance of the TF-IDF and Word2Vec models to represent features in the emotional text classification. We use the support vector machine (SVM) and Multinomial Naïve Bayes (MNB) methods for classification of emotional text on commuter line and transjakarta tweet data. The emotion classification in this study has two steps. The first step classifies data that contain emotion or no emotion. The second step classifies data that contain emotions into five types of emotions i.e. happy, angry, sad, scared, and surprised. This study used three scenarios, namely SVM with TF-IDF, SVM with Word2Vec, and MNB with TF-IDF. The SVM with TF-IDF method generate the highest accuracy compared to other methods in the first dan second steps classification, then followed by the MNB with TF-IDF, and the last is SVM with Word2Vec. Then, the evaluation using precision, recall, and F1-measure results that the SVM with TF-IDF provides the best overall method. This study shows TF-IDF modeling has better performance than Word2Vec modeling and this study improves classification performance results compared to previous studies.
Automatic Ontology Construction Using Text Corpora and Ontology Design Patterns (ODPs) in Alzheimer’s Disease Denis Eka Cahyani; Ito Wasito
Jurnal Ilmu Komputer dan Informasi Vol 10, No 2 (2017): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information
Publisher : Faculty of Computer Science - Universitas Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (679.795 KB) | DOI: 10.21609/jiki.v10i2.374

Abstract

An ontology is defined as an explicit specification of a conceptualization, which is an important tool for modeling, sharing and reuse of domain knowledge. However, ontology construction by hand is a complex and a time consuming task. This research presents a fully automatic method to build bilingual domain ontology from text corpora and ontology design patterns (ODPs) in Alzheimer’s disease. This method combines two approaches: ontology learning from texts and matching with ODPs. It consists of six steps: (i) Term relation extraction (ii) Matching with Alzheimer glossary (iii) Matching with ontology design patterns (iv) Score computation similarity term relation with ODPs (v) Ontology building (vi) Ontology evaluation. The result of ontology composed of 381 terms and 184 relations with 200 new terms and 42 new relations were added. Fully automatic ontology construction has higher complexity, shorter time and reduces role of the expert knowledge to evaluate ontology than manual ontology construction. This proposed method is sufficiently flexible to be applied to other domains.
Pengembangan Sistem Informasi Manajemen Pertanian Menggunakan Framework Codeigniter Untuk Kelompok Tani Desa Bendosewu Blitar Denis Eka Cahyani; Desi Rahmadani; Lucky Tri O; Mahmuddin Yunus
Jurnal KARINOV Vol 4, No 3 (2021): September
Publisher : Institute for Research and Community Service (LP2M), Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um045v4i3p195-200

Abstract

Kelompok tani Tawang Makmur merupakan kelompok tani di Dusun Tawang, Desa Bendosewu yang merupakan penyedia kebutuhan pangan untuk masyarakat khusunya warga Blitar. Tujuan kegiatan pengabdian ini adalah mengembangkan sistem informasi manajemen pertanian untuk kelompok tani desa Bendosewu, Blitar. Sistem informasi manajemen pertanian dibangun menggunakan framework CodeIgniter (CI). Metode yang digunakan adalah tahapan persiapan, pengembangan sistem, sosialisasi, dan evaluasi. Hasil dari pengabdian ini adalah terbangunnya sistem informasi manajemen pertanian untuk kelompok tani. Sistem informasi yang dibangun menyediakan fitur manajemen pertanian seperti pendataan anggota petani, pencatatan pengadaan bahan baku, pendataan penanaman dan hasil panen serta rekapitulasi laporan penanaman dan hasil panen per tahunnya. Selain itu, hasil dari pengabdian ini adalah terbentuknya pemahaman dan keterampilan dari pengelola kelompok tani untuk mengelola sistem informasi guna mendukung peningkatan produktivitas pertanian kelompok tani tersebut. Kata kunci— Sistem informasi manajemen, Pertanian, Kelompok Tani, CodeIgniter  Abstract The Tawang Makmur is a farmer group in Tawang Hamlet, Bendosewu Village which is a provider of food needs for the community, especially Blitar residents. The purpose of this service activity is to develop an agricultural management information system for farmer groups in Bendosewu village, Blitar. The agricultural management information system is built using the CodeIgniter (CI) framework. The method used is the stages of preparation, system development, socialization, and evaluation. The result of this service is the establishment of an agricultural management information system for farmer groups. The information system that was built provides agricultural management features such as data collection on farmer members, recording of raw material procurement, data on planting and harvesting, and recapitulation of annual reports on planting and yields. In addition, the result of this service is the formation of understanding and skills of farmer group managers to manage information systems to support increased agricultural productivity of these farmer groups. Keywords— Management information system, Agriculture, Farmer’s Group, CodeIgniter 
PENERAPAN MACHINE LEARNING UNTUK PREDIKSI PENYAKIT STROKE Denis Eka Cahyani
Jurnal Kajian Matematika dan Aplikasinya (JKMA) Vol 3, No 1 (2022): January
Publisher : UNIVERSITAS NEGERI MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um055v3i12022p15-22

Abstract

Stroke is a global health problem and one of the leading causes of adult disability. Early detection and prompt treatment are needed to minimize further damage to the affected brain area and complications to other parts of the body. Machine learning techniques can be used to predict stroke detection. Machine learning algorithms such as Naive Bayes, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Decision Tree are compared in this study to obtain the best performance in predicting stroke. The implementation stages in this research consist of the pre-processing data, the application of the algorithm and the evaluation and analysis. The Naïve Bayes algorithm obtains better Accuracy, Precision, Recall, and F1-Measure values compared to other algorithms. The values of Accuracy, Precision, Recall, and F1-Measure obtained by Naïve Bayes are 93.93%, 88.23%, 93.93%, and 91.00%, respectively. So the conclusion of this study is that the Naïve Bayes algorithm has the best performance compared to the SVM, KNN and Decision Tree algorithms in predicting stroke.Keywords: decision tree, klasifikasi, k-nearest neighbor, naïve bayes, stroke, support vector machine 
COVID-19 classification using CNN-BiLSTM based on chest X-ray images Denis Eka Cahyani; Anjar Dwi Hariadi; Faisal Farris Setyawan; Langlang Gumilar; Samsul Setumin
Bulletin of Electrical Engineering and Informatics Vol 12, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i3.4848

Abstract

Cases of the COVID-19 virus continue to spread still needs to be considered even though we have entered the post-pandemic era. Rapid identification of COVID-19 cases is necessary to prevent the virus from spreading further. This study developed a chest X-ray-based (CXR) COVID-19 classification for COVID-19 detection using the convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) combination model and compared the CNN-BiLSTM combination model with CNN models. The CNN models used in this study are the transfer learning models, namely Resnet50, VGG19, InceptionV3, Xception, and AlexNet. This research classifies CXR into three groups: COVID-19, normal, and viral pneumonia. In comparison to other models, the Resnet50-BiLSTM model is the most accurate and hence the best. The accuracy of the Resnet50-BiLSTM model was 98.48%. The model that obtains the next highest accuracy i.e Resnet50, VGG19-BiLSTM, VGG19, InceptionV3-BiLSTM, InceptionV3, Xception-BiLSTM, Xception, AlexNet-BiLSTM, and AlexNet. In this study, precision, recall, and F1-measure are also employed to demonstrate that Resnet50-BiLSTM achieves the highest value compared to other approaches. When compared to previous studies, this study enhances classification performance results.
Detecting emotions using a combination of bidirectional encoder representations from transformers embedding and bidirectional long short-term memory Wibawa, Aji Prasetya; Cahyani, Denis Eka; Prasetya, Didik Dwi; Gumilar, Langlang; Nafalski, Andrew
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 6: December 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i6.pp7137-7146

Abstract

One of the most difficult topics in natural language understanding (NLU) is emotion detection in text because human emotions are difficult to understand without knowing facial expressions. Because the structure of Indonesian differs from other languages, this study focuses on emotion detection in Indonesian text. The nine experimental scenarios of this study incorporate word embedding (bidirectional encoder representations from transformers (BERT), Word2Vec, and GloVe) and emotion detection models (bidirectional long short-term memory (BiLSTM), LSTM, and convolutional neural network (CNN)). With values of 88.28%, 88.42%, and 89.20% for Commuter Line, Transjakarta, and Commuter Line+Transjakarta, respectively, BERT-BiLSTM generates the highest accuracy on the data. In general, BiLSTM produces the highest accuracy, followed by LSTM, and finally CNN. When it came to word embedding, BERT embedding outperformed Word2Vec and GloVe. In addition, the BERT-BiLSTM model generates the highest precision, recall, and F1-measure values in each data scenario when compared to other models. According to the results of this study, BERT-BiLSTM can enhance the performance of the classification model when compared to previous studies that only used BERT or BiLSTM for emotion detection in Indonesian texts.
Classification of pediatric pneumonia using ensemble transfer learning convolutional neural network Cahyani, Denis Eka; Hariadi, Anjar Dwi; Setyawan, Faisal Farris; Gumilar, Langlang; Setumin, Samsul
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.7825

Abstract

Pneumonia is a condition characterised by the sudden inflammation of lung tissue, which is triggered by microorganisms such as fungi, viruses, and bacteria. Chest X-ray imaging (CXR) can detect pneumonia, but it requires considerable time and medical expertise. Consequently, the objective of this study is to diagnose pneumonia using CXR imaging in order to effectively detect early cases of pneumonitis in children. The study employs the ensemble transfer learning convolutional neural network (ETL-CNN) transfer learning ensemble, which combines multiple CNN transfer learning models. Resnet50-VGG19 and VGG19-Xception are the ETL-CNN models used in this investigation. Comparing ETL-CNN models to CNN transfer learning models such as Resnet50, VGG19, and Xception. Pediatric CXR pneumonia, which consists of a normal and pneumonia image, is the source of these study results. The results of this analysis indicate that Resnet50-VGG19 achieved the highest level of accuracy, 99.14%. Additionally, the Resnet50-VGG19 obtained the highest levels of precision and recall when comparing to other models. Consequently, the conclusion of this study is that the Resnet50-VGG19 model can generate acceptable classification performance for pediatric pneumonia based on CXR. This study improves classification results for performance when compared to earlier studies.
MESIN ANTRIAN PASIEN BERBASIS WIRELESS COMMUNICATION UNTUK MENINGKATKAN PELAYANAN PASIEN DI PUSKESMAS KEDUNGKANDANG KOTA MALANG Ira Kumalasari; Denis Eka Cahyani; Langlang Gumilar; Achmad Safi’i
Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Vol. 4 (2023): PROSIDING SEMINAR NASIONAL PENGABDIAN KEPADA MASYARAKAT - SNPPM2023
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Negeri Jakarta

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

Abstract

Abstract The Community Health Center (Puskesmas) is the technical implementation unit of the district/city service which is responsible for carrying out health development in a work area. Kedungkandang Community Health Center is one of them. By carrying out the "SIIP" work culture, namely Smiling, Informative, Integrative, Professional and Excellent in service to patients at the Community Health Center. The Kedungkandang Community Health Center serves an average of 75 patients every day. In patient queue services, up to now, manual or conventional methods have been used, with patients taking the keplex/queue number and waiting to be called by the screening officer to ask about the patient's complaint and record it in the patient service book. The service takes a long time and increases theworkload of police officers because they have to call patients one by one without a loudspeaker. Apart from that, there are some patients who are older, usually their hearing is reduced, so when they are called they don't hear or know. This sometimes causes conflict between patients and staff. One of the innovations offered by the PKM (Community Service) team is the creation of a wireless communication-based patient queue machine. It is hoped that this service program will run well by achieving several benefits, namely: Increasing public knowledge regarding the use of wireless communication-based patient queuing machines; Improving excellent health services to the community; Making Kedungkandang Health Center the best health center in terms of health services and becoming a model health center in Malang City. The queuing machine created has specifications: there is running text and automatic voice calls as well as a remote queuing machine for officers based on wireless communication. The following are the methods for implementing community service activities, namely: survey of service locations, problem formulation, needs analysis, product creation and testing, product delivery (socialization and training), activity evaluation and reporting. Abstrak Pusat Kesehatan Masyarakat (Puskesmas) adalah unit pelaksana teknis dinas kabupaten/kota yang bertanggungjawab menyelenggarakan pembangunan kesehatan di suatu wilayah kerja. Puskesmas Kedungkandang adalah salah satunya. Dengan mengusung budaya kerja “SIIP” yaitu Senyum, Informatif, Integratif, Profesional dan Prima dalam pelayanan terhadap pasien di Puskesmas. Puskesmas Kedungkandang setiap harinya melayani pasien dengan jumlah rata-rata 75 pasien. Dalam pelayanan antrian pasien selama ini masih menggunakan cara manual atau konvensional dengan pasien mengambil nomor keplek/antrian dan menunggu dipanggil oleh petugas screening untuk menanyakan keluhan pasien dan mencatatnya dalam buku pelayanan pasien. pelayanan menjadi lama dan menambah beban kerja petugas poli karena harus memanggil pasien satu persatu tanpa pengeras suara. Selain itu ada beberapa pasien yang sudah berumur, biasanya pendengaran mereka berkurang, sehingga ketika dipanggil tidak mendengar atau mengetahui. Hal ini kadang menimbulkan konflik antara pasien dengan petugas. Salah satu inovasi yang ditawarkan oleh tim PKM (Pengabdian Kepada Masyarakat) adalah pembuatan mesin antrian pasien berbasis wirelees communicatioan. Program pengabdian ini diharapkan dapat berjalan dengan baik dengan tercapainya beberapa manfaat yaitu: Meningkatkan pengetahuan masyarakat mengenai penggunaan mesin antrian pasien berbasis wireless communication; Meningkatkan pelayanan kesehatan yang prima kepada masyarakat; Menjadikan Puskesmas Kedungkandang sebagai Puskesmas terbaik dalam segi pelayanan kesehatan dan menjadi percontohan Puskesmas di Kota Malang. Mesin antrian yang dibuat memiliki spesifikasi: terdapat running text dan panggilan suara otomatis serta remote mesin antrian pada petugas berbasis wireless communication. Berikut metode pelaksanaan kegiatan pengabdian masyarakat yaitu: survei lokasi pengabdian, rumusan masalah, analisa kebutuhan, pembuatan produk dan uji coba, penyerahan produk (sosialisasi dan pelatihan), evaluasi kegiatan dan pelaporan.
Co-Authors A.N. Afandi Abu Bakar, Siti Juliana Abu Talib, Muhammad Abbas Achmad Safi’i Afrizal Doewes Aji Prasetya Wibawa Anjar Dwi Hariadi Bambang Harjito, Bambang Che Ani, Adi Izhar Desi Rahmadani Didik Dwi Prasetya Dito Valentino Dityo Kreshna Argeshwara Faisal Farris Setyawan Hariadi, Anjar Dwi Ira Kumalasari Irene Patasik Ito Wasito Junoh, Ahmad Kadri Khavid Wasi Triyoga Langlang Gumilar Lucky Tri O M. Farrel Akbar Firzatullah Mahmuddin Yunus Nafalski, Andrew Samsul Setumin Sari Widya Sihwi Setumin, Samsul Setyawan, Faisal Farris Winda Mustikaningtyas