Articles
<![CDATA[Classification of Sentiments on Twitter Opinions with The Keyword Sinovac Using Naive Bayes ]]>
<![CDATA[Bagus Muhammad Akbar]]>;
<![CDATA[Ahmad Taufiq Akbar]]>;
<![CDATA[Rochmat Husaini]]>
<![CDATA[ Seminar Nasional Informatika (SEMNASIF) Vol 1, No 1 (2021): Inovasi Teknologi dan Pengolahan Informasi untuk Mendukung Transformasi Digital ]]>
Publisher : <![CDATA[Jurusan Teknik Informatika ]]>
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<![CDATA[Tujuan :Menerapkan metode analisis sentimen menggunakan metode Naïve Bayes pada opini twitter mengenai sentimen masyarakat terhadap vaksin covid-19, khususnya jenis sinovac.Metode: Valence Shifter-Lexicon based dan Data mining dengan algoritma Naïve Bayes untuk menentukan kategori sentimen tentang vaksin sinovacHasil : Analisis sentimen menggunakan metode Naïve Bayes menghasilkan 1433 (71,65%) sentimen positif, 403 (20,15%) sentimen negatif, dan 164 (8,2%) untuk sentimen netral. Sedangkan anaisis sentimen menggunakan metode Valence Shifter-lexicon based menghasilkan 903 (45,15%) sentimen positif, 437 (21,85%) sentimen negatif, dan sentimen netral sebesar 660 (33%).. Berdasarkan analisis sentimen dengan 2 metode tersebut, metode Naive bayes lebih signifikan dalam mengklasifikasikan sentimen. Disamping itu, Hasil penelitian ini juga mengisyaratkan bahwa kemunculan vaksin sinovac memberikan kesan positif di kalangan masyarakat.State of the Art:Metode yang digunakan dalam penelitian ini adalah 2 metode yakni Valence Shifter-lexicon based dan Naïve Bayes utuk menentukan sentimen masyarakat tentang vaksin sinovac. Sehingga lebih mengklarifikasi hasil analisis sentimen cenderung positif meskipun tidak dilakukan pelabelan secara manual.]]>
<![CDATA[A proposed method for handling an imbalance data in classification of blood type based on Myers-Briggs type indicator ]]>
<![CDATA[Ahmad Taufiq Akbar]]>;
<![CDATA[Rochmat Husaini]]>;
<![CDATA[Bagus Muhammad Akbar]]>;
<![CDATA[Shoffan Saifullah]]>
<![CDATA[ Jurnal Teknologi dan Sistem Komputer Volume 8, Issue 4, Year 2020 (October 2020) ]]>
Publisher : <![CDATA[Department of Computer Engineering, Engineering Faculty, Universitas Diponegoro ]]>
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DOI: 10.14710/jtsiskom.2020.13625
<![CDATA[Blood type still leads to an assumption about its relation to some personality aspects. This study observes preprocessing methods for improving the classification accuracy of MBTI data to determine blood type. The training and testing data use 250 data from the MBTI questionnaire answers given by 250 respondents. The classification uses the k-Nearest Neighbor (k-NN) algorithm. Without preprocessing, k-NN results in about 32 % accuracy, so it needs some preprocessing to handle data imbalance before the classification. The proposed preprocessing consists of two-stage, the first stage is the unsupervised resample, and the second is the supervised resample. For the validation, it uses ten cross-validations. The result of k-Nearest Neighbor classification after using these proposed preprocessing stages has finally increased the accuracy, F-score, and recall significantly.]]>
<![CDATA[Analisis Sentimen dan Emosi Vaksin Sinovac pada Twitter menggunakan Naïve Bayes dan Valence Shifter ]]>
<![CDATA[Bagus Muhammad Akbar]]>;
<![CDATA[Ahmad Taufiq Akbar]]>;
<![CDATA[Rochmat Husaini]]>
<![CDATA[ Jurnal Teknologi Terpadu Vol. 7 No. 2: December, 2021 ]]>
Publisher : <![CDATA[LPPM STT Terpadu Nurul Fikri ]]>
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DOI: 10.54914/jtt.v7i2.433
<![CDATA[The Sinovac vaccine is among the Covid-19 news in the world in early 2021. That information has led to public responses between the pros and cons. Through Twitter media, the public responds to the issue of the Sinovac; therefore, their opinions on Twitter can analyze to count the percentage of sentiment and emotion towards the Sinovac. This analysis hopes to provide a wise and objective reference, although the pros and cons information is still emerging. This study uses Rstudio for sentiment analysis through Twitter opinion classification using Naïve Bayes and the Valence Shifter Lexicon method to analyze emotions, also using the Naïve Bayes. The Data is 2000 English-language Twitter comments limited to the latest and most popular tweet based on the Sinovac keyword since February 1, 2021, from all Twitter users worldwide. The results showed that Naïve Bayes recognized 1433 (71.65%) positive sentiments, 403 (20.15%) negative sentiments, and 164 (8.2%) neutral sentiments. Meanwhile, Valence Shifter Lexicon recognized 903 (45.15%) positive sentiment, 437 (21.85%) negative sentiment, and 660 (33%) neutral sentiments. The Naïve Bayes also succeeded in recognizing emotions with the highest number 1727 (86.35%) mixed emotions and 141 (7.05%) joy emotion.]]>
<![CDATA[Analysis of Sentiments and Emotions about Sinovac Vaccine Using Naive Bayes ]]>
<![CDATA[Bagus Muhammad Akbar]]>;
<![CDATA[Ahmad Taufiq Akbar]]>;
<![CDATA[Rochmat Husaini]]>
<![CDATA[ Telematika Vol 19, No 2 (2022): Edisi Juni 2022 ]]>
Publisher : <![CDATA[Jurusan Teknik Informatika ]]>
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DOI: 10.31315/telematika.v19i2.7601
<![CDATA[Tujuan:Banyak negara di dunia telah berusaha mengendalikan dampak pandemi COVID-19 melalui penggunaan vaksin. vaksin sinovac merupakan salah satu vaksin populer yang telah digunakan di beberapa negara termasuk Indonesia. Sejak hadirnya vaksin sinovac, persepsi masyarakat baik di lapangan maupun di media sosial semakin muncul antara setuju dan tidak setuju dengan vaksin tersebut. Persepsi masyarakat dunia di media sosial dapat dianalisis untuk mengetahui kategori sentimen dan tingkat emosional masyarakat terhadap penerimaan vaksin Sinovac.Perancangan/metode/pendekatan:Analisis dapat dilakukan melalui data mining yang menggunakan algoritma Naive Bayes untuk menghitung probabilitas dan statistik sehingga setiap opini dapat diklasifikasikan dalam kategori sentimen positif, negatif, atau netral. Dalam penelitian ini, sumber analisis data adalah persepsi publik yang mengandung kata kunci “sinovac” dari twitter. Pengujian menggunakan sentimen, sentimen, dan library syuzhet menunjukkan bahwa sentimen positif lebih tinggi daripada negatif dan netral. Sentimen negatif paling dipengaruhi oleh tingkat emosional kesedihan dan kemarahan. Sedangkan sentimen positif sangat dipengaruhi oleh kategori senang dan emosi campur aduk. Kategori emosi campuran lebih sesuai dengan sentimen positif.Hasil:Klasifikasi emosi terhadap data tweet dalam penelitian ini menunjukkan bahwa kategori emosi kegembiraan, dan campuran memiliki persentase tertinggi yang mengandung polaritas sentimen positif. Berdasarkan penelitian ini, kata kunci sinovac cenderung memunculkan sentimen positif. Polaritas mempengaruhi emosi, namun tidak sebaliknya. Karena terlihat bahwa nilai akurasi pada klasifikasi polaritas (dengan kedua library) telah meningkat ketika fitur emosi tidak diikutkan. Sedangkan nilai akurasi pada klasifikasi emosi justru meningkat ketika fitur polaritas diikutkan.Keaslian/ state of the art:Metode Naive Bayes (library setiment) dan metode Valence Shifter (library sentimentr) yang digunakan dalam analisis sentimen pada penelitian ini menunjukkan bahwa sentimen positif lebih tinggi daripada netral dan negatif. Hasil persentase sentimen positif oleh metode Valence Shifter lebih rendah daripada metode Naive Bayes. Pada metode Valence Shifter cenderung menghasilkan agregat yang lebih kecil antara hasil persentase sentimen positif dibanding netral dan negatif.]]>
<![CDATA[Analysis of the usability quality of vocational high school websites using a user satisfaction approach ]]>
<![CDATA[Rochmat Husaini]]>;
<![CDATA[Bagus Muhammad Akbar]]>;
<![CDATA[Ahmad Taufiq Akbar]]>
<![CDATA[ Telematika Vol 19, No 3 (2022): Edisi Oktober 2022 ]]>
Publisher : <![CDATA[Jurusan Teknik Informatika ]]>
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DOI: 10.31315/telematika.v19i3.7988
<![CDATA[Purpose: knowing the extent to which aspects that affect the level of user/visitor satisfaction in using the website.methodology: the method used is usability approach to measure website visitor satisfaction using Structural Equation Model (SEM) theory and SmartPLS v.3.2.9 software.Findings/result: found several variables that influence user satisfaction, and found variables that had no effect, even having a negative dependency value. In addition, it also produces priority recommendations for website improvement to meet user satisfaction.Originality: this study uses the palmer model usability approach [13] and the structural equation model. Which is different from previous research using the webqual method and Importance Performance Analisys [3]]]>
<![CDATA[Preprocessing Using SMOTE and K-Means for Classification by Logistic Regression on Pima Indian Diabetes Dataset ]]>
<![CDATA[Ahmad Taufiq Akbar]]>;
<![CDATA[Rochmat Husaini]]>;
<![CDATA[Hari Prapcoyo]]>
<![CDATA[ Telematika Vol 20, No 2 (2023): Edisi Juni 2023 ]]>
Publisher : <![CDATA[Jurusan Informatika ]]>
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DOI: 10.31315/telematika.v20i2.9676
<![CDATA[Purpose: Our study aims to combine pre-processing methods to develop a training data model from the Indian diabetic Pima dataset so that it can improve the performance of machine learning in recognizing diabetesDesign/methodology/approach: This research was started through several stages such as collecting the Pima indian diabetes dataset, pre-processing including k-means clustering, oversampling using SMOTE, then undersampling the dataset whose cluster is a minority in each class. Furthermore, the dataset is classified using machine learning namely logistic regression through 10 cross validationFindings/result: The results of this classification performance show that the accuracy reaches 99.5% and is higher than the method in previous studies.Originality/value/state of the art:The method in this study uses SMOTE to handle data imbalances and k-means clustering to remove outliers by removing labels that do not match the majority cluster in each class so that clean data is produced and validation using logistic regression is more accurate than previous studies.Tujuan: Penelitian ini bertujuan untuk menerapkan metode pre-processing untuk membentuk model data latih dari dataset Pima Indian diabetes sehingga dapat meningkatkan performa mesin pembelajaran dalam mengenali diabetes.Perancangan/metode/pendekatan: Riset ini dimulai melalui beberapa tahap yakni pengumpulan dataset Pima Indian diabetes, pre-processing meliputi clustering, oversampling menggunakan SMOTE, kemudian undersampling pada dataset pada klaster minoritas pada setiap kelas. Selanjutnya dataset diklasifikasikan menggunakan machine learning yakni metode regresi logistik melalui 10 cross validationHasil: Hasil dari performa klasifikasi ini menunjukkan akurasi mencapai 99,5% dan lebih tinggi daripada metode pada penelitian sebelumnya.Keaslian/ state of the art: Metode dalam penelitian ini menggunakan SMOTE untuk menangani ketidakseimbangan data dan k-means klastering untuk membuang outlier dengan cara menghapus label yang tidak sesuai dengan klaster mayoritas pada setiap kelas sehingga dihasilkan data yang bersih dan pada validasi menggunakan logistic regression lebih akurat daripada penelitian sebelumnya.]]>
<![CDATA[The Single Sign On Model Using SAML and OAuth for Online Application of UPNYK ]]>
<![CDATA[Ahmad Taufiq Akbar]]>;
<![CDATA[Hari Prapcoyo]]>;
<![CDATA[Rifki Indra Perwira]]>
<![CDATA[ JELIKU (Jurnal Elektronik Ilmu Komputer Udayana) Vol 12 No 4 (2024): JELIKU Volume 12 No 4, May 2024 ]]>
Publisher : <![CDATA[Informatics Department, Faculty of Mathematics and Natural Sciences, Udayana University ]]>
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DOI: 10.24843/JLK.2024.v12.i04.p19
<![CDATA[Big companies have different systems both in terms of applications as well as the operating system, which requires each user to login to each different applications over and over again. With the SSO, users only need to remember one username and one password, but apply automatically universal across enterprise applications, so in this way it can be easier by using SAML (Security Assertion Markup language) for applications to be integrated without having to create a separate user validation. This SAML technology is an XML-based framework and can guarantee the encryption of all or part of the data and then convey it to the end user. Moreover, it allows easy and secure data exchange between systems. The data exchange will be protected by authorization and authentication through tokens containing statements to pass data between users authorized by SAML. SAML can be supported by OAUTH as bearer protocol to provide extensive security when user access services along side on the SSO network]]>
<![CDATA[Evaluation of waiting time for outpatient services at Respira Hospital Yogyakarta using discrete system simulation ]]>
<![CDATA[Astanti, Yuli Dwi]]>;
<![CDATA[Rahmawati, Berty Dwi]]>;
<![CDATA[Akbar, Ahmad Taufiq]]>;
<![CDATA[Rysnalendra, Alya Pangesti]]>
<![CDATA[ OPSI Vol 16, No 2 (2023): ISSN 1693-2102 ]]>
Publisher : <![CDATA[Jurusan Teknik Industri Fakultas Teknologi Industri UPN "Veteran" Yogyakarta ]]>
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DOI: 10.31315/opsi.v16i2.11536
<![CDATA[The Ministry of Health of the Republic of Indonesia has established standard rules for the quality of outpatient service in hospitals. One indicator of the quality of outpatient services at a hospital is the patient's waiting time to be served either by a specialist or other services such as a pharmacy. Respira Hospital Yogyakarta is a special pulmonary and respiratory hospital in Yogyakarta that continues to improve the quality of its services. Based on the results of observations and interviews it is known that in terms of waiting time, patients at Respira Hospital Yogyakarta still have to wait to get service. Examples of queues that occur include patients waiting for a specialist doctor's examination for around 75 to 90 minutes. waiting at the pharmacy and cashier for up to 60 minutes or more. This study attempts to evaluate the waiting time for outpatient services at Respira Hospital Yogyakarta using a simulation. Based on the simulation results, it is known that the patient's waiting time in the system is 217.33 minutes and the longest waiting time is in the pediatric polyclinic and pharmacy departments. After the scenario implementation were made, namely in the pediatric polyclinic and pharmacy sections, the waiting time decreased to 177.19 minutes. This means that evaluations carried out using simulations can help hospitals reduce waiting time for outpatients]]>
<![CDATA[Analisis Sentimen dan Emosi Vaksin Sinovac pada Twitter menggunakan Naïve Bayes dan Valence Shifter ]]>
<![CDATA[Akbar, Bagus Muhammad]]>;
<![CDATA[Akbar, Ahmad Taufiq]]>;
<![CDATA[Husaini, Rochmat]]>
<![CDATA[ Jurnal Teknologi Terpadu Vol 7 No 2: Desember, 2021 ]]>
Publisher : <![CDATA[LPPM STT Terpadu Nurul Fikri ]]>
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DOI: 10.54914/jtt.v7i2.433
<![CDATA[The Sinovac vaccine is among the Covid-19 news in the world in early 2021. That information has led to public responses between the pros and cons. Through Twitter media, the public responds to the issue of the Sinovac; therefore, their opinions on Twitter can analyze to count the percentage of sentiment and emotion towards the Sinovac. This analysis hopes to provide a wise and objective reference, although the pros and cons information is still emerging. This study uses Rstudio for sentiment analysis through Twitter opinion classification using Naïve Bayes and the Valence Shifter Lexicon method to analyze emotions, also using the Naïve Bayes. The Data is 2000 English-language Twitter comments limited to the latest and most popular tweet based on the Sinovac keyword since February 1, 2021, from all Twitter users worldwide. The results showed that Naïve Bayes recognized 1433 (71.65%) positive sentiments, 403 (20.15%) negative sentiments, and 164 (8.2%) neutral sentiments. Meanwhile, Valence Shifter Lexicon recognized 903 (45.15%) positive sentiment, 437 (21.85%) negative sentiment, and 660 (33%) neutral sentiments. The Naïve Bayes also succeeded in recognizing emotions with the highest number 1727 (86.35%) mixed emotions and 141 (7.05%) joy emotion.]]>
<![CDATA[Implementasi Perancangan dan Pemeliharaan Jaringan Internet Menuju Smart School pada MA Raden Fattah ]]>
<![CDATA[Ahmad Taufiq Akbar]]>;
<![CDATA[Bagus Muhammad Akbar]]>;
<![CDATA[Shoffan Saifullah]]>;
<![CDATA[Andiko Putro Suryotomo]]>;
<![CDATA[Rochmat Husaini]]>;
<![CDATA[Hari Prapcoyo]]>
<![CDATA[ Masyarakat Berkarya : Jurnal Pengabdian dan Perubahan Sosial Vol. 2 No. 1 (2025): Februari : Masyarakat Berkarya : Jurnal Pengabdian dan Perubahan Sosial ]]>
Publisher : <![CDATA[Lembaga Pengembangan Kinerja Dosen ]]>
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DOI: 10.62951/karya.v2i1.1079
<![CDATA[Internet Network is one of the fields in informatics and electronics engineering which is now growing rapidly due to the issue of the industrial revolution 4.0 which is increasingly closely related to Cloud computing technology and the Internet of Things. Without resources and knowledge about computer networks, the Internet of things and Cloud computing are quite impossible to design. Computer networks give birth to internet access which is very much needed by every agency and even the entire community in the world. Especially in educational institutions such as Madrasah Aliyah (MA) Raden Fatah, which is located in Kalasan, Yogyakarta when in the era of the Covid-19 pandemic, it faces the challenge of disruption from offline learning to online learning. To answer the demands of the times, MA Raden Fattah is very enthusiastic in developing its institution towards a quality smart school. The network infrastructure available at MA Raden Fattah has not been optimized, so through this service, network design and management are carried out so that the need for access points that help students and teachers can be met. This service has succeeded in increasing the number of access points, optimizing the management of internet network resources at MA Raden Fattah, and improving the quality of teaching and learning services at the institution]]>
