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All Journal International Journal of Electrical and Computer Engineering Jurnal Masyarakat Informatika MATEMATIKA Jurnal INKOM Techno.Com: Jurnal Teknologi Informasi Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) Rekayasa: Jurnal Penerapan Teknologi dan Pembelajaran Sinergi International Journal of Advances in Intelligent Informatics Scientific Journal of Informatics Journal of Information Systems Engineering and Business Intelligence Journal of Information System IT JOURNAL RESEARCH AND DEVELOPMENT
Retno Kusumaningrum
Universitas Diponegoro
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics

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Suitability analysis of rice varieties using learning vector quantization and remote sensing images Annisa Apriliani; Retno Kusumaningrum; Sukmawati Nur Endah; Yudo Prasetyo
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 3: June 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i3.12234

Abstract

Rice (Oryza Sativa) is the main food for Indonesian people, thus maintaining the stability of rice production in Indonesia becomes an important issue for further study. A strategy to overcome the issue is to apply precision agriculture (PA) using remote sensing images as a reference due to its effectiveness. The initial stage of PA is suitability analysis of rice varieties, including INPARA, INPARI, and INPAGO. While the representative features that can be extracted from remote sensing images and related to agriculture field are NDVI, NDWI, NDSI, and BI. Therefore, the aim of this study is to identify the best model for analyzing the most suitable superior rice varieties using Learning Vector Quantization. The results show that the best LVQ model is obtained at learning rate value of 0.001, epsilon value of 0.1, and the features combination of NDWI and BI values (in standard deviation). The architecture generates accuracy value of 56%.
Solid waste classification using pyramid scene parsing network segmentation and combined features Khadijah Khadijah; Sukmawati Nur Endah; Retno Kusumaningrum; Rismiyati Rismiyati; Priyo Sidik Sasongko; Iffa Zainan Nisa
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 19, No 6: December 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v19i6.18402

Abstract

Solid waste problem become a serious issue for the countries around the world since the amount of generated solid waste increase annually. As an effort to reduce and reuse of solid waste, a classification of solid waste image is needed  to support automatic waste sorting. In the image classification task, image segmentation and feature extraction play important roles. This research applies recent deep leaning-based segmentation, namely pyramid scene parsing network (PSPNet). We also use various combination of image feature extraction (color, texture, and shape) to search for the best combination of features. As a comparison, we also perform experiment without using segmentation to see the effect of PSPNet. Then, support vector machine (SVM) is applied in the end as classification algorithm. Based on the result of experiment, it can be concluded that generally applying segmentation provide better source for feature extraction, especially in color and shape feature, hence increase the accuracy of classifier. It is also observed that the most important feature in this problem is color feature. However, the accuracy of classifier increase if additional features are introduced. The highest accuracy of 76.49% is achieved when PSPNet segmentation is applied and all combination of features are used.
Continuous speech segmentation using local adaptive thresholding technique in the blocking block area method Roihan Auliya Ulfattah; Sukmawati Nur Endah; Retno Kusumaningrum; Satriyo Adhy
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 18, No 1: February 2020
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v18i1.13958

Abstract

Continuous speech is a form of natural human speech that is continuous without a clear boundary between words. In continuous speech recognition, a segmentation process is needed to cut the sentence at the boundary of each word. Segmentation becomes an important step because a speech can be recognized from the word segments produced by this process. The segmentation process in this study was carried out using local adaptive thresholding technique in the blocking block area method. This study aims to conduct performance comparisons for five local adaptive thresholding methods (Niblack, Sauvola, Bradley, Guanglei Xiong and Bernsen) in continuous speech segmentation to obtain the best method and optimum parameter values. Based on the results of the study, Niblack method is concluded as the best method for continuous speech segmentation in Indonesian language with the accuracy value of 95%, and the optimum parameter values for such method are window = 75 and k = 0.2.
Sentiment analysis of Indonesian hotel reviews: from classical machine learning to deep learning Retno Kusumaningrum; Iffa Zainan Nisa; Rizka Putri Nawangsari; Adi Wibowo
International Journal of Advances in Intelligent Informatics Vol 7, No 3 (2021): November 2021
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v7i3.737

Abstract

Currently, there are a large number of hotel reviews on the Internet that need to be evaluated to turn the data into practicable information. Deep learning has excellent capabilities for recognizing this type of data. With the advances in deep learning paradigms, many algorithms have been developed that can be used in sentiment analysis tasks. In this study, we aim to compare the performance of classical machine learning algorithms—logistic regression (LR), naïve Bayes (NB), and support vector machine (SVM) using the Word2Vec model in conjunction with deep learning algorithms such as a convolutional neural network (CNN) to classify hotel reviews on the Traveloka website into positive or negative classes. Both learning methods apply hyperparameter tuning to determine the parameters that produce the best model. Furthermore, the Word2Vec model parameters use the skip-gram model, hierarchical softmax evaluation, and the value of 100 vector dimensions. The highest average accuracy obtained was 98.08% by using the CNN with a dropout of 0.2, Tanh as convolution activation, softmax as output activation, and Adam as the optimizer. The findings from the study demonstrate that the integration of the Word2Vec model and the CNN model obtains significantly better accuracy than other classical machine learning methods.
Temperament detection based on Twitter data: classical machine learning versus deep learning Annisa Ulizulfa; Retno Kusumaningrum; Khadijah Khadijah; Rismiyati Rismiyati
International Journal of Advances in Intelligent Informatics Vol 8, No 1 (2022): March 2022
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v8i1.692

Abstract

Deep learning has shown promising results in various text-based classification tasks. However, deep learning performance is affected by the number of data, i.e., when the number of data is small, deep learning algorithms do not perform well, and vice versa. Classical machine learning algorithms commonly work well for a few data, and their performance reaches an optimal value and does not increase with the increase in sample data. Therefore, this study aimed to compare the performance of classical machine learning and deep learning methods to detect temperament based on Indonesian Twitter. In this study, the proposed Indonesian Linguistic Inquiry and Word Count were employed to analyze the context of Twitter. The classical machine learning methods implemented were support vector machine and K-nearest neighbor, whereas the deep learning method employed was a convolutional neural network (CNN) with three different architectures. Both learning methods were implemented using multiclass classification and one versus all (OVA) multiclass classification. The highest average f-measure was 58.73%, obtained by CNN OVA with a pool size of 3, a dropout value of 0.7, and a learning rate value of 0.0007.
Relevance Feedback using Genetic Algorithm on Information Retrieval for Indonesian Language Documents Salman Dziyaul Azmi; Retno Kusumaningrum
Journal of Information Systems Engineering and Business Intelligence Vol. 5 No. 2 (2019): October
Publisher : Universitas Airlangga

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (727.1 KB) | DOI: 10.20473/jisebi.5.2.171-182

Abstract

Background: The Rapid growth of technological developments in Indonesia had resulted in a growing amount of information. Therefore, a new information retrieval environment is necessary for finding documents that are in accordance with the user’s information needs.Objective: The purpose of this study is to uncover the differences between using Relevance Feedback (RF) with genetic algorithm and standard information retrieval systems without relevance feedback for the Indonesian language documents.Methods: The standard Information Retrieval (IR) System uses Sastrawi stemmer and Vector Space Model, while Genetic Algorithm-based (GA-based) relevance feedback uses Roulette-wheel selection and crossover recombination. The evaluation metrics are Mean Average Precision (MAP) and average recall based on user judgments.Results: By using two Indonesian language document datasets, namely abstract thesis and news dataset, the results show 15.2% and 28.6% increase in the corresponding MAP values for both datasets as opposed to the standard Information Retrieval System. A respective 7.1% and 10.5% improvement on the recall value at 10th position was also observed for both datasets. The best obtained genetic algorithm parameters for abstract thesis datasets were a population size of 20 with 0.7 crossover probability and 0.2 mutation probability, while for news dataset, the best obtained genetic algorithm parameters were a population size of 10 with 0.5 crossover probability and 0.2 mutation probability.Conclusion: Genetic Algorithm-based relevance feedback increases both values of MAP and average recall at 10th position of retrieved document. Generally, the best genetic algorithm parameters are as follows, mutation probability is 0.2, whereas the size of population size and crossover probability depends on the size of dataset and length of the query.Keywords: Genetic Algorithm, Information Retrieval, Indonesian language document, Mean Average Precision, Relevance Feedback 
CIELab Color Moments: Alternative Descriptors for LANDSAT Images Classification System Retno Kusumaningrum; Hisar Maruli Manurung; Aniati Murni Arymurthy
INKOM Journal Vol 8, No 2 (2014)
Publisher : Pusat Penelitian Informatika - LIPI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.inkom.409

Abstract

This study compares the image classification system based on normalized difference vegetation index (NDVI) and Latent Dirichlet Allocation (LDA) using CIELab color moments as image descriptors.  It was implemented for LANDSAT images classification by evaluating the accuracy values of classification systems. The aim of this study is to evaluate whether the CIELab color moments can be used as an alternatif descriptor replacing NDVI when it is implemented using LDA-based classification model.  The result shows that the LDA-based image classification system using CIELab color moments provides better performance accuracy than the NDVI-based image classification system, i.e 87.43% and 86.25% for LDA-based and NDVI-based respectively.  Therefore, we conclude that the CIELab color moments which are implemented under the LDA-based image classification system can be assigned as alternative image descriptors for the remote sensing image classification systems with the limited data availability, especially when the data only available in true color composite images.
Analisa Tingkat Penerimaan Pengguna Terhadap Aplikasi SIMPUS dengan Metode Technology Acceptance Model (TAM) Sri Mulyono; Wahyul Amien Syafei; Retno Kusumaningrum
JOINS (Journal of Information System) Vol 5, No 1 (2020): Edisi Mei 2020
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2372.674 KB) | DOI: 10.33633/joins.v5i1.3277

Abstract

Penelitian ini bertujuan melakukan Analisa Tingkat Penerimaan PenggunaTerhadap pengguna Apikasi Sistem Informasi Manajemen Puskesmas SIMPUS dengan metode TAM. Variabel yang digunakan yaitu persepsi tentang kemudahan (Perseive ease of use), Persepsi terhadap kemanfaatan (Perceived Usefulness), Sikappenggunaan (Attitude Toward Using), Perilaku untuk tetap menggunakan (Behavioral Intention To Use), Kondisi nyata pengguna sistem (Actual System Usage).Bahan dan alat penelitian adalah Aplikasi SIMPUS.Teknik pengumpulan data dengan observasi dan penyebaran kuisioner tertutupsejumlah 110 kuisioner kepada 110 pengguna aplikasi SIMPUS sebagai sampel.Hasil penelitian menunjukan pada data  keenam variable berdistribusi normal (nilai Sig > 0.05) yaitu X1  (p= 0,172), X2 (p=0,171), X3 (p=0,143), X4 (p=0,117), X5 (p=0,062), Y (p=0,592).Hasil Uji Linearitas dengan menggunakan uji Durbin Watson menunjukkan bahwa tidak auto korelasi atau asumsi terpenuhi bahwa fungsi linier yaitu nilai statistic Durbin Watson (1,722) terletak diantara nilai table Durbin Watson (1,703) dan 4 – DU (2,291).Hasil Uji multikolinieritas menunjukkan bahwa tidak ada satu pun variable bebas yang memiliki nilai VIF(variance inflation factor) lebih dari 10 atau nilai toleransi kurang dari 0,10.  Dan nilaiFhitung =91,469>Ftabel=2,65 atau nilai p<0,05.Variabel  X1, X2, X3,X4, X5berpengaruh secara simultan atau bersama – sama dan signifikan terhadap penerimaan SIMPUS.Kesimpulan aspek persepsi kemudahan, persepsi kegunaan,sikap menggunakan, memberikan pengaruh nyata terhadap penerimaan SIMPUS. Sedangkan niat perilaku menggunakan, penggunaan SIMPUS sesungguhnya tidak berpengaruh terhadap penerimaan SIMPUS.
Ensemble Classifier untuk Klasifikasi Kanker Payudara Khadijah Khadijah; Retno Kusumaningrum
IT Journal Research and Development Vol. 4 No. 1 (2019)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (356.594 KB) | DOI: 10.25299/itjrd.2019.vol4(1).3540

Abstract

Kanker payudara merupakan jenis kanker yang paling banyak diderita oleh kaum wanita di Indonesia. Penyakit tersebut dapat berakibat pada kematian jika terlambat ditangani. Oleh karena itu, deteksi dini kanker payudara merupakan langkah awal untuk menyelamatkan nyawa pasien. Pada penelitian ini telah dilakukan klasifikasi kanker payudara berdasarkan data anthopometric serta data dari hasil tes darah rutin menggunakan single classifier (ELM, SVM dan kNN) dan ensemble classifier yang menggabungkan ketiga algoritma tersebut dengan penentuan kelas majority voting. Pembagian data dilakukan dengan three way data split. Hasil eksperimen menunjukkan bahwa saat menggunakan keseluruhan fitur penggunaan ensemble classifier lebih baik daripada single classifier dalam hal akurasi maupun G-mean. Namun, saat menggunakan 4 fitur terbaik (resistin, glucose, age, dan BMI) penggunaan ensemble classifier sedikit lebih baik dalam hal G-mean. Hal ini disebabkan minimnya diversity di antara classifier sehingga saat digabungkan tidak mampu memperbaiki hasil.
Co-Authors Adi Wibowo Aniati Murni Arymurthy Annisa Apriliani Annisa Ulizulfa Apriliyanto, Andi Bambang Subeno Helmie Arif Wibawa Hisar Maruli Manurung Iffa Zainan Nisa Juwanda, Farikhin Khadijah, Khadijah Manurung, Hisar Maruli Prajanto Wahyu Adi Prastya, Dwiyan Panji Priyo Sidik Sasongko Rismiyati Rismiyati Rismiyati Rismiyati Rizka Putri Nawangsari Roihan Auliya Ulfattah Salman Dziyaul Azmi Satriyo Adhy Sri Mulyono Sukmawati Nur Endah Sukmawati Nur Endah Suryono Suryono Wahyul Amien Syafei Widiyanto Yeny Rochmawati, Yeny Yudo Prasetyo