Article Per Year (5 Year)
p-Index From 2021 - 2026
0.817
P-Index
This Author published in this journals
All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Siasat Bisnis Jurnal Buana Informatika JUITA : Jurnal Informatika International Journal of Advances in Intelligent Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Anny Kartika Sari
Universitas Gadjah Mada
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Engineering Social Sciences

Published : 14 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 14 Documents
Search

 1   2 
CONTENT BASED IMAGE RETRIEVAL BERDASARKAN FITUR LOW LEVEL: LITERATURE REVIEW Hidayat, Rahmad; Harjoko, Agus; Sari, Anny Kartika
Jurnal Buana Informatika Vol 8, No 2 (2017): Jurnal Buana Informatika Volume 8 Nomor 2 April 2017
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v8i2.1077

Abstract

Abstract. Content-based Image Retrieval (CBIR) is an image search process by comparing the image features sought by the images contained in the database. Low-level features in the image are commonly used in CBIR is the color, texture, and shape. This article conducts a review of journals related to CBIR, particularly research based on low-level features. The journals are then classified based on the color space, features and feature extraction methods. The results show that the color space often used is the RGB and HSV due to their compatibility with the hardware and human perception of color. The features most often used in CBIR is the color feature. This is due to the fact that color features can easily and quickly be extracted. The most often used method to extract the color feature is the color histogram, the most common method used to extract texture features is the gray level co-occurence matrix, and the method most widely used to extract the shape feature is canny edge.Keywords: CBIR, color, texture, shape. Abstrak. Content based Image Retrieval (CBIR) merupakan proses pencarian gambar dengan membandingkan fitur-fitur yang terdapat pada gambar yang dicari dengan gambar yang terdapat dalam basis data. Fitur-fitur low level pada gambar yang biasa digunakan dalam CBIR adalah warna, tekstur, dan bentuk Artikel ini melakukan tinjauan terhadap penelitian-penelitian yang berkaitan dengan CBIR, khususnya penelitian yang berbasis pada fitur low level. Penelitian-penelitian tersebut kemudian diklasifikasikan berdasarkan ruang warna, fitur dan metode ekstraksi fitur. Hasil tinjauan menunjukkan bahwa ruang warna yang sering digunakan adalah RGB dan HSV karena dianggap cocok dengan hardware dan persepsi manusia terhadap warna. Adapun fitur yang paling sering digunakan dalam CBIR adalah fitur warna. Hal ini disebabkan fitur warna mudah dan cepat diekstraksi. Metode yang paling sering digunakan untuk mengekstraksi fitur warna adalah histogram warna, metode yang paling sering digunakan untuk mengekstraksi fitur tekstur adalah gray level co-occurence matrix, dan metode yang paling banyak digunakan untuk, mengekstraksi fitur bentuk adalah canny edge.Kata kunci: CBIR, warna, tekstur, bentuk.
Sistematic Review: Model Peramalan Wabah Penyakit Demam Berdarah Agus Qomaruddin Munir; Anny Kartika Sari
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2015
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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

Abstract

Penyebaran wabah penyakit demam berdarahdengue (DBD) secara global dengan tingkat frekuensi yangcenderung tinggi pada kurun waktu 50 tahun terakhirmemunculkan sebuah gagasan pencegahan yang sistematis.Epidemiologi DBD memberikan pola pencegahan terhadapwabah DBD untuk menangulangi masalah tersebut.Ulasan ini bertujuan memberikan gambaran secara sistematisterhadap pemodelan peramalan (forecast) data DBD denganpendekatan pola spasial dan spatio-temporal yang menghasilkanpeta risiko kejadian DBD. Untuk mendapatkan hasil peramalanyang tepat harus memperhatikan variabel peramalan/prediktor.Variabel prediktor memiliki kategori yang terdiri atas variabeldemografis dan sosial ekonomi (misal: usia jenis kelamin,pendidikan, dan kondisi wilayah tempat tinggal). Diantara faktorlingkungan yang mempunyai pengaruh signifikan adalah curahhujan dan suhu udara. Peta deskriptif memberikan informasititik lokasi DBD (dengue hotspot) yang berguna untukmengidentifikasi besaran resiko pada suatu wilayah.Tinjauan dilihat dari beberapa kategori yaitu trend risetpenyakit DBD, dari aspek bidang ilmu epidemiologi, forecastingmodel, statistical model, dan spasial model. Selanjutnya dariaspek teknis sistem yang dihasilkan dengan melihat dampak,kontribusi, ketepatan (accuracy) dan algoritma. Tersedianyasumber daya, kelayakan akuisisi, kualitas data, di sampingkeahlian teknis yang tersedia, menentukan akurasi modelperamalan dan peta risiko DBD serta penerapannya dalambidang kesehatan masyarakat. Beberapa variabel prediktor yangtidak diketahui menimbulkan tantangan dan membatasikemampuan untuk menghasilkan model peramalan dan petarisiko yang efektif dan menjadi faktor kegagalan dalampengembangan sistem.
Challenges of Sarcasm Detection for Social Network : A Literature Review Afiyati Afiyati; Azhari Azhari; Anny Kartika Sari; Abdul Karim
JUITA : Jurnal Informatika JUITA Vol. 8 Nomor 2, November 2020
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1012.329 KB) | DOI: 10.30595/juita.v8i2.8709

Abstract

Nowadays, sarcasm recognition and detection simplified with various domains knowledge, among others, computer science, social science, psychology, mathematics, and many more. This article aims to explain trends in sentiment analysis especially sarcasm detection in the last ten years and its direction in the future. We review journals with the title’s keyword “sarcasm” and published from the year 2008 until 2018. The articles were classified based on the most frequently discussed topics among others: the dataset, pre-processing, annotations, approaches, features, context, and methods used. The significant increase in the number of articles on “sarcasm” in recent years indicates that research in this area still has enormous opportunities. The research about “sarcasm” also became very interesting because only a few researchers offer solutions for unstructured language. Some hybrid approaches using classification and feature extraction are used to identify the sarcasm sentence using deep learning models. This article will provide a further explanation of the most widely used algorithms for sarcasm detection with object social media. At the end of this article also shown that the critical aspect of research on sarcasm sentence that could be done in the future is dataset usage with various languages that cover unstructured data problem with contextual information will effectively detect sarcasm sentence and will improve the existing performance.
Integrated AHP, Profile Matching, and TOPSIS for selecting type of goats based on environmental and financial criteria Clara Hetty Primasari; Retantyo Wardoyo; Anny Kartika Sari
International Journal of Advances in Intelligent Informatics Vol 4, No 1 (2018): March 2018
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v4i1.105

Abstract

Goat farm businessman should considered environmental and financial criteria in breeding their commodities. The environmental factors are temperature, humidity, rain intensity, and altitude. For financial criteria, used several sub criteria i.e NPV (Net Present Value), ROI (Return On Investment), BCR (Benefit Cost Ratio), PBP (Payback Period), and BEP (Break Event Point) to determine financial feasibility. This research aims to develop a decision support system for selecting type of goat to breed by combining AHP, Profile Matching, and TOPSIS. AHP method was used for calculating the weight, Profile Matching for environment suitability evaluation, and TOPSIS for producing a valid decision that represents the goat expert's decision. The result showed that three methods can be integrated, and an experimental results which was validated by expert show that Bligon goat had the highest preference value (0.8835847). This can be concluded that DSS decision was valid and it successfully represented expert’s consideration.
A Web Based Expert System for Identifying Bloomed Plants Anny Kartika Sari; Adriana Sari Aryani
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 1, No 1 (2006): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.18

Abstract

This research discusses the development of a web based expert system for identifying bloomed plants. The identification is based on the seven visible features of a bloomed plant, i.e. the root type, the type of the plant, the shape of the flowers, the shape of the leaves, the height of the plant, and the length and the width of the leaves. For inference process, the forward chaining method is used. The system is developed using PHP as the programming language and MySQL as the database management system. Based on some testing conducted to the system, it can be concluded that the system can identify bloomed plants with the accuracy of 100%. The system can accommodate the update on its knowledge as long as the update is only on the available features in the system. This drawback can be used as starting point for the future development of the system.
Optimization of ARIMA Forecasting Model using Firefly Algorithm Ilham unggara; Aina Musdholifah; Anny Kartika Sari
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 2 (2019): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.37666

Abstract

 Time series prediction aims to control or recognize the behavior of the system based on the data in a certain period of time. One of the most widely used method in time series prediction is ARIMA (Autoregressive Integrated Moving Average). However, ARIMA has a weakness in determining the optimal model. firefly algorithm is used to optimize ARIMA model (p, d, q). by finding the smallest AIC (Akaike Information Criterion) value in determining the best ARIMA model. The data used in the study are daily stock data JCI period January 2013 until August 2016 and data of foreign tourist visits to Indonesia period January 1988 to November 2017.Based on testing, for JCI data, obtained predicted results with Box-Jenkins ARIMA model produces RMSE 49.72, whereas the prediction with the ARIMA Optimization model yielded RMSE 49.48. For the data of Foreign Tourist Visits, the predicted results with the Box-Jenkins ARIMA model resulted in RMSE 46088.9, whereas the predicted results with ARIMA optimization resulted in RMSE 44678.4. From these results it can be concluded that the optimization of ARIMA model with Firefly Algorithm produces better forecasting model than ARIMA model without Optimization.
Sarcasm Detection For Sentiment Analysis in Indonesian Tweets Yessi Yunitasari; Aina Musdholifah; Anny Kartika Sari
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 1 (2019): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.41136

Abstract

Twitter is one of the social medias that are widely used at the moment. Tweet conversations can be classified according to their sentiments. The existence of sarcasm contained in a tweet sometimes causes incorrect determination of the tweet’s sentiment because sarcasm is difficult to analyze automatically, even by humans. Hence, sarcasm detection needs to be conducted, which is expected to improve the results of sentiment analysis. The effect of sarcasm detection on sentiment analysis can be seen in terms of accuracy, precision and recall. In this paper, detection of sarcasm is applied to Indonesian tweets. The feature extraction of sarcasm detection uses unigram and 4 Boazizi feature sets which consist of sentiment-relate features, punctuation-relate features, lexical and syntactic features, and top word features. Detection of sarcasm uses the Random Forest algorithm. The feature extraction of sentiment analysis uses TF-IDF, while the classification uses Naïve Bayes algorithm. The evaluation shows that sentiment analysis with sarcasm detection improves the  accuracy of sentiment analysis about 5.49%. The accuracy of the model is 80.4%, while the precision is 83.2%, and the recall is 91.3%.
The K-Means Clustering Algorithm With Semantic Similarity To Estimate The Cost of Hospitalization Ida Bagus Gede Sarasvananda; Retantyo Wardoyo; Anny Kartika Sari
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 4 (2019): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.45093

Abstract

 The cost of hospitalization from a patient can be estimated by performing a cluster of patient. One of the algorithms that is widely used for clustering is K-means. K-means algorithm, based on distance still has weaknesses in terms of measuring the proximity of meaning or semantics between data. To overcome this problem, semantic similarity can be used to measure the similarity between objects in clustering, so that, semantic proximity can be calculated. This study aims to conduct clustering of patient data by paying attention to the similarity of the patient’s disease. ICD code is used as a guide in determining a patient’s disease. The K-means method is combined with semantic similarity to measure the proximity of the patient’s ICD code. The method used to measure the semantic similarity between data, in this study, is the semantic similarity of Girardi, Leacock & Chodorow, Rada, and Jaccard Similarity. Cluster quality measurement uses the silhouette coefficient method. Based on the experimental results, the method of measuring semantic similarity data is capable to produce better quality clustering results than without semantic similarity. The best accuracy is 91.78% for the three semantic similarity methods, whereas without semantic similarity the best accuracy is 84.93%.
Steganographic Model for encrypted messages based on DNA Encoding Alfian Abdul Jalid; Agus Harjoko; Anny Kartika Sari
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 1 (2021): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.61767

Abstract

Information has become an inseparable part of human life. Some information that is considered important, such as state or company documents, require more security to ensure its confidentiality. One way of securing information is by hiding the information in certain media using steganography techniques. Steganography is a method of hiding information into other files to make it invisible. One of the most frequently used steganographic methods is Least Significant Bit (LSB).In this study, the LSB method will be modified using DNA Encoding and Chargaff's Rule. Chargaff's Rule or complementary base pairing rule is used to construct a complementary strand. The modification of the LSB method using DNA encoding and Chargaff's Rule is expected to increase the security of the information.The MSE test results show the average value of the LSB method is 0.000236368, while the average value for the DNA Encoding-based Steganography method is 0.000770917. The average PSNR value for the LSB method was 76.82 dB while the DNA Encoding-based Steganography method had an average value of 70.88 dB. The time of inserting and extracting messages using the Steganography method based on DNA Encoding is relatively longer than the LSB method because of its higher algorithmic complexity. The message security of the DNA Encoding-based Steganography method is better because there is encryption in the algorithm compared to the LSB method which does not have encryption.
Pengaruh integrasi, berbagi informasi, dan penundaan pada kinerja rantai pasokan: Studi pada usaha kecil menengah batik di Indonesia Ihwan Addin Mufaqih; Nurul Indarti; Wakhid Slamet Ciptono; Anny Kartikasari
Jurnal Siasat Bisnis Vol 21, No 1 (2017)
Publisher : Management Development Centre (MDC) Department of Management, Faculty of Business and Economics Universitas Islam Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20885/jsb.vol21.iss1.art2

Abstract

Penelitian ini bertujuan menguji pengaruh praktik-praktik rantai pasokan yang terdiri dari integrasi pemasok, integrasi internal, integrasi pelanggan, berbagi informasi, dan penundaan (postponement)) terhadap kinerja rantai pasokan. Survei menggunakan kuesioner dilakukan pada 102 UKM Batik, di Surakarta dengan teknik purposive. Hasil penelitian menunjukkan bahwa secara keseluruhan praktik-praktik rantai pasokan berpengaruh terhadap kinerja rantai pasokan. Integrasi internal dan berbagi informasi merupakan praktik-praktik yang berpengaruh positif pada kinerja rantai pasokan. Sementara itu, integrasi pemasok dan integrasi pelanggan tidak berpengaruh pada kinerja rantai pasokan. Menariknya, studi ini menemukan bahwa penundaan berpengaruh negatif terhadap kinerja rantai pasokan.
Co-Authors Abdul Karim Aditya Permana Adriana Sari Aryani Afiyati, Afiyati Agus Harjoko Agus Harjoko Agus Qomaruddin Munir Agus Sihabuddin Aina Musdholifah Aina Musdholifah Alfian Abdul Jalid Astrid Noviana Paradhita Azhari Azhari Clara Hetty Primasari Ida Bagus Gede Sarasvananda Ihwan Addin Mufaqih Ilham unggara Khabib Mustofa Nurul Indarti Retantyo Wardoyo Retantyo Wardoyo Rosa Delima Timothy K. Shih Wakhid Slamet Ciptono Yessi Yunitasari