cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota surakarta,
Jawa tengah
INDONESIA
Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika
Published by Universitas Muhammadiyah Surakarta
ISSN : 2621038X     EISSN : 2477698X     DOI : -
Core Subject : Science,
Computer Science & IT
Khazanah Informatika: Jurnal Ilmiah Komputer dan Informatika, an Indonesian national journal, publishes high quality research papers in the broad field of Informatics and Computer Science, which encompasses software engineering, information system development, computer systems, computer network, algorithms and computation, and social impact of information and telecommunication technology.
Arjuna Subject : -
Articles 250 Documents
 6   7   8   9   10 
PENERAPAN ALGORITMA AES DAN KONVERSI SMS KE DALAM BAHASA KHEK PADA APLIKASI ENKRIPSI BERBASIS MOBILE APPLICATION Kirana, Chandra; Sugianto, Edi
Khazanah Informatika Vol. 5 No. 1 June 2019
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i1.7453

Abstract

Telepon Selular (ponsel) atau dikenal dengan nama HP (handphone) memiliki banyak keunggulan dan kelebihan baik dari segi fasilitas yang dimilikinya, salah satu fasilitas yang banyak digunakan berupa SMS. Akan tetapi fasilitas yang berupa SMS ini memiliki kerentanan berupa penyadapan, maka dari itu diusulkan sebuah aplikasi enkripsi menggunakan algoritma AES dan konversi bahasa khek. Algortima Advanced Encryption Standard (AES) merupakan algoritma kriptografi modern yang bersifat simetris. Pada algoritma AES kunci yang dipakai memiliki panjang bervariasi yaitu 128,192,256 dengan memiliki jumlah ronde yang berbeda pula tergantung panjang kunci-nya. Bahasa Khek adalah bahasa yang dituturkan oleh orang Hakka, memiliki 9 jenis dialek, salah yang digunakan adalah dialek lufang. Dengan menerapkan algoritma AES dan konversi bahasa khek pada aplikasi enkripsi, pengamanan pesan yang dikirim akan terjamin kerahasiaan nya, sehingga pihak yang tidak berwenang, tidak dapat mendapatkan informasi pesan yang dikirimkan. Dalam pengembangan aplikasi selanjutnya diharapkan aplikasi yang dibangun dapat mempunyai fasilitas untuk menyembunyikan sebuah folder yang digunakan untuk menyimpan hasil enkripsi dan juga dekripsi.
EFFECTIVENESS OF SVM METHOD BY NAïVE BAYES WEIGHTING IN MOVIE REVIEW CLASSIFICATION Zain, Fadli Fauzi; Sibaroni, Yuliant
Khazanah Informatika Vol. 5 No. 2 December 2019
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i2.7770

Abstract

Classification of movie review belongs to the realm of text classification, especially in the field of sentiment analysis. One familiar text classification method used is support vector maching (SVM) and Naïve Bayes. Both of these methods are known to have good performance in handling text classification separately. Combining these two methods is expected to improve the performance of classifier compared to working separately. This paper reports the effort to classify movie reviews using the combined method of Naïve Bayes and SVM with Naïve Bayes as weights. This combined method is commonly called NBSVM. The results showed the best accuracy is obtained if the classification is done by the NBSVM method, which is equal to 88.8% with the combined features of unigram and bigram and using pre-processing in the form of data cleansing only.
ARCHITECTURE OF BACK PROPAGATION NEURAL NETWORK MODEL FOR EARLY DETECTION OF TENDENCY TO TYPE B PERSONALITY DISORDERS Hayat, Cynthia; Limong, Samuel; Sagala, Noviyanti
Khazanah Informatika Vol. 5 No. 2 December 2019
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i2.7923

Abstract

Personal disorder is a type of mental illness. People with personal disorder can not respond changes and demands of life in normal ways. Women with type B personal disorder tend to have high risk of violence. It is important to make early detetction of this personal disorder, so that it can be anticipated properly. This paper reports an architecture model of back propagation neural network (BPPN) for early detection of type B personal disorder. The back propagation process divided into two phases, i.e training and testing. The training process used 43 data and the testing process used 34 data. The output classified into 4 diagnosis category of type B personal disorder, I.e. anti social, borderline, histrionic, and narcissistics. The optimal parameters of BPPN model consist of maximum epoch of 1000, maximum mu of 10000000000, increase mu of 25, decrease mu of 0.1, and neuron hidden layer of 25. The MSE of training is 3.07E-14 and MSE of testing is 1.00E-03. The accuracy of training is 90.7%, while the accuracy of testing is 97.2%.
APLIKASI MUSICROID SEBAGAI MEDIA PEMBELAJARAN SENI MUSIK BERBASIS ANDROID Purwanto, Adi; Widaningrum, Ida; Fitri, Khoiru Nur
Khazanah Informatika Vol. 5 No. 1 June 2019
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i1.7772

Abstract

Musik merupakan salah satu cabang seni yang telah lama ada di dunia. Musik membuat hidup ini semakin berwarna, kemanapun dan dimanapun tanpa kita sadari berdampingan dengan musik.­ Penelitian ini tentang media pembelajaran yang menarik untuk pembelajaran seni musik, terutama guitar dan piano untuk membantu siswa yang kesulitan memahami konsep chord dan cara memainkannya. Chord Theory perlu dipahami siswa karena menjadi dasar permainan alat musik. Penelitian ini menggunakan model ADDIE, yang meliputi 5 tahapan yaitu: Analysis, Design, Development, Implementation dan  Evaluation. Pengembangan media pembelajaran seni musik ini disebut ?Musicroid?. Musicroid menggunakan sistem Android yang saat ini dikenal sebagai sistem operasi Open Source. Pengembangan dilakukan dengan menggunakan software Android Studio yang didukung dengan pemrograman bahasa Java. Hasil yang diperoleh dari hasil uji validasi ahli materi dengan nilai 4,6 dan ahli media dengan nilai 4,3 termasuk kategori sangat layak. Uji kelayakan faktor usability memperoleh nilai 5,8, dikategorikan sangat layak sebagai media pembelajaran musik.
ANALYSIS OF SLOW MOVING GOODS CLASSIFICATION TECHNIQUE: RANDOM FOREST AND NAïVE BAYES Jollyta, Deny; Gusrianty, Gusrianty; Sukrianto, Darmanta
Khazanah Informatika Vol. 5 No. 2 December 2019
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i2.8263

Abstract

Classifications techniques in data mining are useful for grouping data based on the related criteria and history. Categorization of goods into slow moving group or the other is important because it affects the policy of the selling. Various classification algorithms are available to predict labels or class labels of data. Two of them are Random Forest and Naïve Bayes. Both algorithms have the ability to describe predictions in detail through indicators of accuracy, precision, and recall. This study aims to compare the performance of the two algorithms, which uses testing data of snacks with labels for package type, size, flavor and categories. The study attempts to analyze data patterns and decides whether or not the goods fall into the slow moving category. Our research shows that Random Forest algorithm predicts well with accuracy of 87.33%, precision of 85.82% and recall of 100%. The aforementioned algorithm performs better than Naïve Bayes algorithm which attains accuracy of 84.67%, precision of 88.33% and recall of 92.17%. Furthermore, Random Forest algorithm attains AUC value of 0.975 which is slightly higher than that attained by Naïve Bayes at 0.936. Random Forest algorithm is considered better based on the value of the metrics, which is reasonable because the algorithm does not produce bias and is very stable.
CASE BASE REASONING (CBR) AND DENSITY BASED SPATIAL CLUSTERING APPLICATION WITH NOISE (DBSCAN)-BASED INDEXING IN MEDICAL EXPERT SYSTEMS Santoso, Herdiesel; Musdholifah, Aina
Khazanah Informatika Vol. 5 No. 2 December 2019
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v5i2.8323

Abstract

Case-based Reasoning (CBR) has been widely applied in the medical expert systems. CBR has computational time constraints if there are too many old cases on the case base. Cluster analysis can be used as an indexing method to speed up searching in the case retrieval process. This paper propose retrieval method using Density Based Spatial Clustering Application with Noise (DBSCAN) for indexing and cosine similarity for the relevant cluster searching process. Three medical test data, that are malnutrition disease data, heart disease data and thyroid disease data, are used to measure the performance of the proposed method. Comparative tests conducted between DBSCAN and Self-organizing maps (SOM) for the indexing method, as well as between Manhattan distance similarity, Euclidean distance similarity and Minkowski distance similarity for calculating the similarity of cases. The result of testing on malnutrition and heart disease data shows that CBR with cluster-indexing has better accuracy and shorter processing time than non-indexing CBR. In the case of thyroid disease, CBR with cluster-indexing has a better average retrieval time, but the accuracy of non-indexing CBR is better than cluster indexing CBR. Compared to SOM algorithm, DBSCAN algorithm produces better accuracy and faster process to perform clustering and retrieval. Meanwhile, of the three methods of similarity, the Minkowski distance method produces the highest accuracy at the threshold ? 90.
WRITER IDENTIFICATION OF LAMPUNG HANDWRITTEN DOCUMENTS BASED ON SELECTED CHARACTERS Junaidi, Akmal; Trianingsih, Syifa; Iqbal, Muhammad
Khazanah Informatika Vol. 6 No. 1 April 2020
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v6i1.8418

Abstract

Writer identification is a sub-field in handwriting recognition which its objective is to determine the identity of the writer based on handwriting input. The goal is usually for forensic purposes such as finding the perpetrators of crimes that leave traces of evidence in the form of written messages. In addition, writer identification can also be used to determine the identity of a historical actor if he or she leaves a valuable written artefact. The object of this research is the traditional character of the Lampung region which is so-called Had Lampung by the local community. The traditional character of Lampung consists of 20 main characters and 12 diacritics. Based on selected characters, the writer will be recognized using the Principal Component Analysis (PCA) feature. PCA is one linear feature extraction method of an object in pattern recognition. The PCA algorithm consists of several stages, namely the calculation of the average dataset, the subtraction of the vector dataset with averages, the calculation of covariance, the calculation of eigenvectors and eigenvalues, eigenvector reduction, and the projection of the dataset against reduced eigenvector space. PCA in this paper is used as a feature in image recognition. The dataset utilized in this study is the Lampung Dataset which is a handwritten character recognition (HWCR) dataset. Lampung Dataset consists of 82 Lampung handwritten documents. All Lampung character images in the dataset were extracted from these documents using the connected component extraction algorithm and eventually generated 32,140 images. Furthermore, these images are converted into grayscale images. In this research, as many as 12,500 grayscale images of Lampung handwriting characters were chosen to represent 82 different writers. This data is employed as training and testing data on the proposed method. The highest accuracy of the identification of the writer using this PCA feature is 82.92%, while the lowest accuracy is 28.29%.
PERFORMANCE OF METHODS IN IDENTIFYING SIMILAR LANGUAGES BASED ON STRING TO WORD VECTOR Sujaini, Herry
Khazanah Informatika Vol. 6 No. 1 April 2020
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v6i1.8199

Abstract

Indonesia has a large number of local languages that have cognate words, some of which have similarities among each other. Automatic identification within a family of languages faces problems, so it is necessary to learn the best performer of language identification methods in doing the task. This study made an effort to identification Indonesian local languages, which used String to Word Vector approach. A string vector refers to a collection of ordered words. In a string vector, a word is represented as an element or value, while the word becomes an attribute or feature in each numeric vector. Among Naïve Bayes, SMO, J48, and ZeroR classifiers, SMO is found to be the most accurate classifier with a level of accuracy at 95.7% for 10-fold cross-validation and 94.4% for 60%: 40%. The best tokenizer in this classification is Character N-Gram. All classifiers, except ZeroR shows increased accuracy when using Character N-Gram Tokenizer compared to Word Tokenizer. The best features of this system are the TriGram and FourGram Character. The TriGram is preferred because it requires smaller training data. The highest accuracy value in the combination experiment is 0.965 obtained at a combination of IDF = FALSE and WC = TRUE, regardless the conditions of the TF.
MARKET BASKET ANALYSIS TO IDENTIFY STOCK HANDLING PATTERNS & ITEM ARRANGEMENT PATTERNS USING APRIORI ALGORITHMS Prawira, Tresna Yudha; Sunardi, Sunardi; Fadlil, Abdul
Khazanah Informatika Vol. 6 No. 1 April 2020
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v6i1.8628

Abstract

The process of managing the pattern of handling stock of goods and the pattern of arranging goods on store shelves requires an identification process by utilizing data from sales transaction results. Market basket analysis of sales transaction data using Apriori Algorithm stages produces an information in the form of association rules with a minimum support value of 50% and a minimum confidence of 60%. It can be a reference in the arrangement of items on store shelves by referring to a combination of items that are often bought by consumers simultaneously. In addition, the stock inventory pattern can take advantage of the results of determining the high frequency value in the combination pattern 1 - itemset C1 with a minimum support value of 50% which is compared with the initial inventory.
MAPPING LAND SUITABILITY FOR SUGAR CANE PRODUCTION USING K-MEANS ALGORITHM WITH LEAFLETS LIBRARY TO SUPPORT FOOD SOVEREIGNTY IN CENTRAL JAVA Seta, Pramudhita Tunjung; Hartomo, Kristoko Dwi
Khazanah Informatika Vol. 6 No. 1 April 2020
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/khif.v6i1.9027

Abstract

Indonesia is the largest sugar importer country in the world, this is contrary to the government's desire to realize sugar self-sufficiency. To overcome the dependence on sugar imports in order to support national food sovereignty, geographic information system technology (GIS) can be used to present information as material for consideration by the government in determining policies on the management of sugar cane land resources. The K-means algorithm is used to group regions according to production level, while the Matching method is for evaluating the suitability of sugarcane land. Presentation of data in the form of map visualization on the web using a new model in processing land data, where this model processes production grouping data, and land suitability class data in the form of GeoJSON then mapped with the help of Leaflets. This new model enables dynamic land data processing and visualization in the form of interactive maps. The results of the EUCS test for GIS mapping of Land Suitability and Cane Production are 3.23 (Satisfied) of the total score of 4, so this system can be accepted by the user.

Page 8 of 25 | Total Record : 250

 6   7   8   9   10 

Filter by Year

2015 2023


Reset
Filter By Issues
All Issue Vol. 9 No. 2 October 2023 Vol. 9 No. 1 April 2023 Vol. 8 No. 2 October 2022 Vol. 8 No. 1 April 2022 Vol. 7 No. 2 October 2021 Vol. 7 No. 1 April 2021 Vol. 6 No. 2 October 2020 Vol. 6 No. 1 April 2020 Vol. 5 No. 2 December 2019 Vol. 5 No. 1 Juni 2019 Vol. 5 No. 1 June 2019 Vol. 4 No. 2 Desember 2018 Vol. 4 No. 2 Desember 2018 Vol. 4 No. 1 Juni 2018 Vol. 4 No. 1 Juni 2018 Vol. 3 No. 2 Desember 2017 Vol. 3 No. 2 Desember 2017 Vol. 3 No. 1 Juni 2017 Vol. 3 No. 1 Juni 2017 Vol. 2 No. 2 Desember 2016 Vol. 2 No. 2 Desember 2016 Vol. 2 No. 1 Juni 2016 Vol. 2 No. 1 Juni 2016 Vol. 1 No. 1 Desember 2015 Vol. 1 No. 1 Desember 2015 More Issue