Information technology has an important role in carrying out company management activities. It is important that information technology is managed properly so that no risks arise that could endanger ...the company. Companies can implement information technology risk management through risk management audits. An audit on information technology risk management can help evaluate companies by identifying information technology risks and minimizing information technology risks. Such audits can be carried out with the help of the COBIT framework. This study intends to conduct a systematic literature review on risk management audits related to information technology using the COBIT framework. Literature search from IEEXplore, ScienceDirect and Garuda Kemdikbud database sources. Papers were selected based on inclusion criteria. Inclusion criteria include paper language is Indonesian and English, paper is published between 2019-2023, the paper describes COBIT in IT risk management audits, and paper is available as full text. The results obtained were 24 papers. There are two criteria for assessing paper quality, namely the paper contains the COBIT framework used for IT risk management audits and the paper contains the COBIT domain used. The results of the analysis of research questions indicate that COBIT 5 is a guide used by many researchers in information technology audits for risk management. COBIT 5 provides a complete and comprehensive risk governance guide for measuring enterprise IT risk management. Implementation of COBIT 5 in IT risk management audits to assist in risk assessment and risk management in order to minimize and prevent IT risks that may occur. Domain APO12 (Manage Risk) and EDM03 (Ensure Risk Optimization) as a reference in conducting IT risk management.
Komoditas jagung di Indonesia menjadi tanaman pangan terbesar kedua setelah padi sebagai sumber karbohidrat. Namun dikarenakan keterbatasan kemampuan petani dan faktor lingkungan menyebabkan upaya ...penanganan tanaman jagung akibat adanya serangan organisme pengganggu tanaman menjadi terhambat. Penelitian ini mengusulkan upaya deteksi secara dini terhadap jenis penyakit pada daun tanaman jagung menggunakan metode Convolutional Neural Network (CNN) yang dikenal sebagai algoritma pembelajaran mesin berkinerja tinggi dalam mengklasifikasikan jenis penyakit tanaman ke dalam beberapa kelas seperti Blight, Common Rust, Grey Leaf Spot, dan Healthy. Selain itu, transformasi warna citra dari RGB, HSV dan Grayscale, proses segmentasi dengan Region of Interest (ROI) serta dilengkapi dengan penerapan ektraksi fitur tekstur dengan menggunakan GLCM telah mampu menghasilkan tingkat akurasi sebesar 94% dan nilai loss rate yang relatif kecil yaitu 0.1742. Hasil penelitian ini menunjukkan bahwa penggunaan metode CNN terbukti secara efisien & efektif dalam melakukan identifikasi jenis penyakit tanaman.
The presence of online media has shifted print media for news readers to get information that is fast, accurate, and easy to access. However, the problem arises because the length of the news text ...makes the reader bored to search for the news as a whole so the news that is obtained will be less accurate. For this reason, it is necessary to have an automatic text summary that was raised in this study as well as to compare the Maximum Marginal Relevance (MMR) algorithm and the LexRank algorithm to the summary of Indonesian news texts on the online news portal graphanews. com. the results of the comparison test of text summarization using f-measure , precision and recall show the performance of text summarization with the MMR algorithm is better where f-measure is 91.65%, precision is 91.08% and recall is 92.23%.
One of the methods used to improve the performance of text summarization to obtain complete information in a learning module is by transforming the words in a module into basic words or, in other ...words, through a steaming process. The steaming process in Indonesian language texts is more complicated/complex because there are word affixes that must be removed to get the root word (root word) of a word, so this research will compare the two stemming algorithms of Porter and stemming Nazief & Adriani in the learning module at Mataram University of Technology. The test results of the Nazief & Adriani stemming algorithm on an average process duration of 51.8 seconds with an average accuracy of 74.175%. In Porter's Algorithm, the average processing time is 16.875 seconds, with an accuracy of 73.225%.
Komentar di Instagram sangat berharga, informatif dan sangat membantu. Bagi penjual komentar adalah fitur yang menunjukkan respons pengguna Instagram terhadap produk yang ditawarkan, dan melalui ...fitur komentar penjual dapat menemukan pelanggan yang potensial. Manfaat tersebut diperoleh apabila penjual melakukan analisis pada komentar di toko Instagram-nya. Sangat dimungkinkan untuk menganalisis secara manual apabila data komentar pada tokonya berjumlah sedikit namun apabila komentar yang dimiliki banyak maka akan lebih cepat apabila menggunakan sistem. Banyaknya spam dapat mengganggu informasi yang ada pada komentar, sehingga tidak menjamin banyaknya komentar pada sebuah posting-an maka banyak pula yang ingin membeli produk tersebut. Oleh karena itu dibutuhkan sistem yang bisa memfilter komentar agar penjual dapat menemukan pelanggan yang tepat untuk produknya. Penelitian ini menggunakan algoritma TF-IDF untuk mengklasifikasikan komentar ke dalam 2 kelas (potensial dan tidak potensial) dan memperoleh akurasi sebesar 80%, presisi 0,76 dan recall 0,94. Berdasarkan hasil penelitian pada 294 komentar, 27% di antaranya adalah komentar tidak potensial. Kata yang menunjukkan minat beli seseorang adalah “berapa”, ”kak”, ”ada”, dan ”tidak”, sedangkan kata dominan pada komentar tidak potensial adalah kata “mention” yang menunjukkan aktivitas mention.
The Rabin-Karp algorithm is used to detect similarity using hashing techniques, from related studies modifications have been made in the hashing process but in previous studies have not been ...conducted research for the best k value in the K-Gram process. At the stage of stemming the Nazief & Adriani algorithm is used to transform the words into basic words. The researcher uses several variations of K-Gram values to determine the best K-Gram values. The analysis was performed using Ukara Enhanced public data obtained from the Kaggle with a total of 12215 data. The student essay answers data totaled to 258 data in the group A and 305 in the group B, every student essay answers data in each group will be compared with the answers of other fellow group member. Research results are the value of k = 3 has the best performance which has the highest some interpretations of 1-14% (Little degree of similarity) and 15-50% (Medium level of similarity) compared to values of k = 5, 7, and 9 which have the highest number of interpretation results 0%-0.99% (Document is different). However, if the students essay answers compared have 100% (Exactly the same) interpretations, the k value on K-Gram does not affect the results.
Central Processing Unit (CPU) and External Graphics Processing Unit (eGPU) technology are known as overclocks which aim to make the device exceed the benchmarks set by the device maker. Until now ...there is no determination to rank the two hardware within certain limits such as hardware price range and year-by-year. Therefore, it is necessary to process the ranking of the hardware using Simple Additive Weighting (SAW) to obtain a ranking range and determine the weight per type of hardware analyzed. It can be classified using Naïve Bayes to determine results of criteria combination between two hardware to determine possible criteria into "not good" and "good". This classification used to determine probability criteria of choosing a combination of CPU and eGPU hardware. The results of this study are getting the best CPU and eGPU every year using SAW and then classifying it for pricing. In testing conducted on application of Naïve Bayes using 80% of training data has 2776 data and 20% of testing data has 695 data that will be tested for accuracy, precision, recall, and F1-score. For results of tests that have been carried out get 0.78 accuracy results, precision 1, Recall 0.764, and F1-Score 0.866.
Data in the timeline of social media users consists of data in the form of text, images, audio, and video. Large and unstructured data in social media can be processed using various techniques such ...as text processing or image processing. In this study, the processed text data is used to classify Twitter users’ personality based on the DISC framework. Out of the initial collected 292 users, we semi-automatically filtered them for only personal accounts with Indonesian language posts. For being able to observe and assess a user’s personality out of their tweets choice of words, we made relevant keyword vocabularies corresponding to DISC framework and theory. There are four experiment scenarios done in this study, with variations on whether the keywords and text data are stemmed or not, and the keywords frequency calculation being weighted or not. Weighting the keywords using the current number in calculation based on their level does not show positive results, neither does stemming as the best results are shown by the not stemmed and not weighted scenario. This study is a preliminary research for an automatic profiling system which employs a combination of Natural Language Processing and Machine Learning approaches.
Modifikasi Fonem Vokal Pada Stemming Kata Tidak Baku Iskandar, Ahmad Fikri; Utami, Ema; Hidayat, Wahyu ...
Jurnal teknologi informasi dan ilmu komputer (Online),
02/2023, Letnik:
10, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Bahasa Indonesia termasuk bahasa yang paling populer digunakan di dunia. Bahasa Indonesia dapat berupa bahasa baku dan tidak baku. Bahasa tidak baku dapat dikarenakan oleh penyerapan dari bahasa ...asing atau bahasa daerah. Penyerapan ini dapat terjadi perganti huruf vokal. Kontribusi pada penelitian ini adalah melakukan modifikasi fonem pada huruf vokal untuk mengembalikan kata tidak baku ke dalam bentuk kata dasar yang baku disebut sebagai Modified Vocal Phonemes Non Formal. Percobaan dilakukan dengan 60 kata tidak baku yang sudah dilakukan preprocessing pada penelitian sebelumnya terlebih dahulu. Penelitian ini membandingkan hasil algoritma dengan algoritma pada penelitian sebelumnya. Algoritma Modified Vocal Phonemes Non Formal telah berhasil melakukan stemming dengan presisi 90.00% dengan 54 kata tidak baku yang sukses dikonversi ke kata dasar sesuai dengan Kamus Besar Bahasa Indonesia (KBBI) dan 6 kata masih belum berhasil dikonversi.
Abstract
Indonesian is one of the most popular languages spoken in the world. Indonesian can be standard and non-standard language. Non-standard language can be caused by absorption of foreign languages or village languages. This absorption can occur as a substitute for vowels. The contribution to this research is to modify the phonemes of vowels to return non-formal words into formal root forms known as Modified Vocal Phonemes in Non-Formal. The experiment was carried out with 60 non-formal words that have been preprocessed in the previous research. This research will compare the results of the algorithm with the algorithm in previous research. Algorithm Modified Vocal Phenomes Non-Formal has succeeded in performing stemming with 90.0% precision with 54 words that were successfully converted to base words according to the Big Indonesian Dictionary and 6 words were still not converted.
Word Analysis of Indonesian Keirsey Temperament Iskandar, Ahmad Fikri; Utami, Ema; Prasetio, Agung Budi
IJCCS (Indonesian Journal of Computing and Cybernetics Systems),
10/2020, Letnik:
14, Številka:
4
Journal Article
Recenzirano
Odprti dostop
Personality uniquely relates to our feeling and pattern to the aspect of actions. This behavior will change through the experience, formal education, and the surrounding environment. This works based ...on the Keirsey Temperament Sorter, a personality questionnaire developed by David Keirsey. This model divides the personality into four categories as Idealists, Rationals, Guardians, and Artisans. This concept is commonly recognized for the interpretation of specialist trends, potentially contributes to the process of recruitment or selection, and potential fields for analysis of social media data. Words selected by using Chi-Square with an error of 5%. Accuracy of the lexicon approach is 34%, while the best machine learning approach is Random Forest algorithm with 69.59%
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