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All Journal Teknika Jupiter Biota: Jurnal Ilmiah Ilmu-Ilmu Hayati Pendidikan Bahasa Inggris Jurnal Informatika Jurnal Informatika Proceeding International Conference on Information Technology and Business JOURNAL OF HEALTHCARE TECHNOLOGY AND MEDICINE JURNAL MEDIA INFORMATIKA BUDIDARMA Jurnal TAM (Technology Acceptance Model) Jurnal Informasi dan Komputer Aptisi Transactions on Technopreneurship (ATT) Jurnal Inovasi Pendidikan Building of Informatics, Technology and Science SERAMBI: Jurnal Ekonomi Manajemen dan Bisnis Islam Sainstek : Jurnal Sains dan Teknologi Bulletin of Computer Science Research Educate: Jurnal Ilmu Pendidikan dan Pengajaran Aurelia: Jurnal Penelitian dan Pengabdian Masyarakat Indonesia Jurnal Ilmiah Mahasiswa Pendidikan Agama Islam Jurnal Informatika: Jurnal Pengembangan IT Jurnal Ilmu Siber dan Teknologi Digital INOVTEK Polbeng - Seri Informatika Jurnal TAM (Technology Acceptance Model)
Chairani Chairani
Institut Informatika Dan Bisnis Darmajaya
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IMPLEMENTASI SISTEM PAKAR UNTUK MENDIAGNOSA KERUSAKAN PRINTER JENIS CANON BJC-2100SP chairani chairani; Sriyanto Sriyanto; Fitria .
Jurnal Informatika Vol 12, No 1 (2012): Jurnal Informatika
Publisher : IIB Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30873/ji.v12i1.135

Abstract

Printer as the printing tool is already dominate several field, such as office, shop, school, university, educational institution, etc. In certain time, there must be trouble in printer using, just like another machines, damages. So that, no matter how hi-tech the printing tool, it must need the expert to fix the damages.Expert system transform the knowledge of the expert(s) to be a computer system that help general people to do anything like an expert. In this case, expert system is built to diagnose the damage of printer type Canon BJC-2100SP. This expert system will increase time of effort to diagnose the damage of the printer type Canon BJC-2100SP for computer technician, specially for printer type Canon BJC-2100SP. Keyword : Production system, expert system, Rule, Knowledge, Inference Engineering.
VISUALISASI PETA RSS FINGERPRINT DALAM FASE OFFLINE PADA LOCALIZATION DI LANTAI 3 GEDUNG TEKNIK ELEKTRO UGM MENGGUNAKAN WLAN chairani chairani; Widyawan Widyawan
Jurnal Informatika Vol 13, No 1 (2013): Jurnal Informatika
Publisher : IIB Darmajaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30873/ji.v13i1.127

Abstract

This paper discusses about the fingerprint mapping of Received Signal Strength (RSS) WLAN, placed in UGM’s Electrical Engineering (TE) building on 3rd Floor Corridor. RSS WLAN measurements performed by using visualization NetSurveyor software and RSS fingerprint map visualization created using RapidMiner software. Dimension area of 3rd floor of UGM’s TE building is 1696.68 m2 and formed in 1893 grids for each grid is 1 m x 1 m. Measurement of RSS WLAN to 5 access points performed in duration of 1 minute 58 seconds, and produce as many as 86980 record. The results of this research is fingerprint visualization map as calibration of RSS WLAN distribution at JTETI building on the 3rd floor which can be used for the development of indoor localization. Keywords : Fingerprint, RSS, WLAN, Localization.  
COMPARISON OF TREE IMPLEMENTATION, REGRESSION LOGISTICS, AND RANDOM FOREST TO DETECT IRIS TYPES Siti Mukodimah; Chairani Fauzi
Jurnal TAM (Technology Acceptance Model) Vol 12, No 2 (2021): Jurnal TAM (Technology Acceptance Model)
Publisher : LPPM STMIK Pringsewu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/jurnaltam.v12i2.1074

Abstract

Iris is a genus of 260-300 species of flowering plants with striking flower colors and has a dominant color in each region. The name iris is taken from the Greek word for rainbow, which is also the name for the Greek goddess of the rainbow, Iris. The number of types of iris plants with almost the same physical characteristics, especially in the pistil and crown, causes the misdetection of iris plant types. Iris plants are deliberately used because data is already available digitally on the internet and software such as orange and is widely used as a material for classifying objects. This research was conducted to classify iris plant types using three algorithms, namely Tree algorithm, Regression Logistics, and Random Forest. Classification algorithms are a learning method for predicting the value of a group of attributes in describing and distinguishing a class of data or concepts that aim to predict a class of objects whose class labels are unknown. The results showed the largest AUC (Area Under Curve) value obtained by the Random Forest method. AUC accuracy is said to be perfect when the AUC value reaches 1,000 and the accuracy is poor if the AUC value is below 0.500. As for the precision value of the three models used Random Forest has the highest precision value. From the data tests that have been done training and testing can be seen that the level of accuracy of testing of the three models where the Random Forest model is superior as a method for classification of irises.
Mobile Application for Computer Laboratory Maintenance in Institute of Informatics and Business (IIB) Darmajaya Yuni Arkhiansyah; Rio Kurniawan; Sulyono Sulyono; Chairani Chairani; Rian Sefriadi
Prosiding International conference on Information Technology and Business (ICITB) 2020: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 6
Publisher : Proceeding International Conference on Information Technology and Business

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

Abstract

The problem statement of this study was that checking and monitoring process in the computer laboratory of Institute of Informatics and Business (IIB) Darmajaya was less optimal because it was still carried out manually. This caused a difficulty for the head and assistants of the computer laboratory because the written data on paper were vulnerable, and easy to lose and scatter. According to this problem, an application was needed not only for the assistants of the computer laboratory who used it to record the checked-list data but also for the head of the computer laboratory who used it to monitor the readiness of the laboratory in real time. It was expected that the application was able to facilitate the checking and monitoring process for the assistants of the computer laboratory. Moreover, the application was expected to facilitate the head of the computer laboratory to monitor the readiness of the computer laboratory by looking at checked lists data stored in the database.Keywords: Monitoring, Real Time, Computer Laboratory
Twitter Sentiment Analysis on The use of Sinovac Vaccine in Indonesia Sherly Trisnawati; Akhmad Unggul Priantoro; Chairani Fauzi; Riko Herwanto; Hari Sabita
Prosiding International conference on Information Technology and Business (ICITB) 2021: INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY AND BUSINESS (ICITB) 7
Publisher : Proceeding International Conference on Information Technology and Business

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

Abstract

Coronavirus Diseases (Covid-19) was reported the first time in Wuhan, Hubeu Province, China 2019. On March 11 th 2021 World Health Organization (WHO) declared covid-19 as a world pandemic. To reduce the number of deathsand the number of the transmission of covid-19 is by the vaccination. Several vaccines have been evaluated by WHO to against covid-19, Indonesian Government officially announced the use of Sinovac Vaccine produced by Sinovac Life ScienceCo, China. This vaccination topic becomes one of the massive topics discussed by the Indonesian people and various responses on social media such as Twitter. The technique used, is crawling tweets from Twitter users worldwide using theEnglish language from May 24 th 2021 – August 31 2021 with the keyword “sinovac vaccine”. This study aims to analyze public sentiment regarding the usage of sinovac vaccine in Indonesia. The method used is Naïve Bayes because it has a simple algorithm with high accuracy. The result shows the classification accuracy rate is 80.99% and majorities’ responds are neutral and positive. However, the preprocessing data has the rule to get excellent result for the accuracy. Future studycan also classify the tweets into different queries and other classification methods can be applied such as Super Vector Machine and KNN. Keywords— Covid-19, Sinovac Vaccine, Sentiment Analysis, Naïve Bayes
Perbandingan Long Short Term Memory (LSTM) dan Gated Recurrent Unit (GRU) Untuk Memprediksi Curah Hujan M Devid Alam Carnegie; Chairani Chairani
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6213

Abstract

One of the impacts of the threat caused by heavy rain is flooding, which can have negative effects on human life. There are many factors that contribute to heavy rain, and predicting the intensity of rainfall issued by BMKG (Meteorology, Climatology, and Geophysics Agency) is an initial solution for planning and taking actions to mitigate the impacts of natural disasters. Machine learning methods can be used to predict weather parameters, especially time series rainfall. Deep learning, a branch of machine learning that can understand patterns and make weather parameter predictions with high accuracy, includes several algorithms commonly used for analyzing and predicting weather parameters, namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU). This research aims to compare both algorithms and determine which one performs best in predicting rainfall at the North Lampung Geophysics Station. From the evaluation results with RMSE (Root Mean Square Error) value of 16.81, MSE (Mean Square Error) value of 282.55, and MAD (Mean Absolute Deviation) value of 10.43, it is known that the LSTM model 1 with a dataset split of 7:3 has the best performance in predicting rainfall. As for the rain prediction, the GRU model 1 with a dataset split of 7:3 performs best with an accuracy value of 62%, precision of 58%, recall of 66%, and f1score of 62%.
Penerapan Algorimta Backpropagation Untuk Prakiraan Cuaca Harian Dibandingkan Dengan Support Vector Machine dan Logistic Regression Ayu Zulfiani; Chairani Fauzi
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 7, No 3 (2023): Juli 2023
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v7i3.6173

Abstract

To anticipate the impacts caused by extreme weather, the Meteorology, Climatology, and Geophysics Agency (BMKG) issues weather forecasts so that the community can be prepared when such extreme weather occurs. The application of Artificial Neural Network (ANN) techniques in weather forecasting significantly enhances the ability to explore vast amounts of big data in obtaining the necessary information, serving as a reliable assistant for forecasting and policymaking. The data used in this study consists of weather elements such as pressure, air temperature, humidity, wind direction and speed, as well as rainfall, obtained from the Radin Inten II Lampung Meteorological Station. The observational data has a data density per hour, spanning a period of 5 years from January 1, 2018, to December 31, 2022. The method employed in this research is Backpropagation Neural Network (BPNN). The research results indicate that BPNN can effectively predict classified rainfall compared to other methods, within recall value when slight rain 0.68, moderate rain 0.17, and heavy rain 0.03, meanwhile Support Vector Machine (SVM) and Logistic Regression (LR) method can predict only slight rain with recall value when slight rain is 0.51 and 0.47.
Perbandingan Akurasi Metode Deteksi Ujaran Kebencian dalam Postingan Twitter Menggunakan Metode SVM dan Decision Trees yang Dioptimalkan dengan Adaboost Yuda Septiawan; Chairani Chairani
TEKNIKA Vol. 17 No. 2 (2023): Teknika Juli - Desember 2023
Publisher : Politeknik Negeri Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.8175025

Abstract

Pada era digital saat ini, akses informasi dan transaksi online telah memberikan kemudahan dan efisiensi bagi manusia. Di Indonesia, peningkatan jumlah penduduk yang terkoneksi internet menjadi 77,02% pada tahun 2021-2022. Namun, penggunaan media sosial juga membawa dampak negatif, termasuk penyebaran konten berbahasa negatif dan ujaran kebencian oleh sebagian oknum. Ujaran kebencian, yang melanggar hukum, memiliki efek samping seperti pembungkaman, penindasan, dan penyebaran rasa benci serta kondisi tidak nyaman lainnya. Oleh karena itu, banyak penelitian dilakukan untuk mendeteksi dan mengurangi penyebaran ujaran kebencian di media sosial, baik di Twitter maupun Instagram. Beberapa penelitian menggunakan metode word2vec dengan skip-gram, TextCNN, dan random oversampling untuk mendeteksi ujaran kebencian pada komentar Instagram. Metode ini menghasilkan akurasi terbaik dengan skor F1 sebesar 93,70%. Selain itu, penelitian juga mencoba menggunakan algoritma GRU dan LSTM dalam pelatihan dan pengujian model untuk meningkatkan kinerja deteksi. Algoritma Latent Dirichlet Allocation (LDA) juga digunakan untuk mengekstrak topik dari tweet dan menganalisis sentimen pada setiap tweet. Berdasarkan penelitian-penelitian tersebut, peneliti akan melakukan pengujian deteksi teks menggunakan algoritma K-Nearest Neighbor, dan Decision Tree terhadap ujaran kebencian pada postingan Twitter dengan tagar #indonesia. Pengumpulan data dilakukan melalui API Twitter dengan implementasi menggunakan bahasa pemrograman Python. Data tweet akan dilabeli menggunakan sentistrength, dan kemudian dilakukan klasifikasi menggunakan metode K-Nearest Neighbor, dan Decision Tree untuk menentukan sentimen pada data tersebut. Topik ini penting untuk diteliti karena penyebaran ujaran kebencian di media sosial dapat memiliki dampak negatif yang signifikan. Dengan menggunakan pendekatan dan metode yang tepat, penelitian ini bertujuan untuk mengembangkan alat pendeteksi yang efektif untuk melindungi individu dan masyarakat dari dampak negatif ujaran kebencian di media sosial.
Data Driven Analysis of Borobudur Ticket Sentiment Using Naïve Bayes. Dedi Kundana; Chairani
Aptisi Transactions On Technopreneurship (ATT) Vol 5 No 2sp (2023): Special Issue: Support Technopreneurship in the Medical
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v5i2sp.353

Abstract

 The recent growth of social media is hugely influential and plays a significant role in various aspects of people's lives in the digital era. Twitter is a social media network that is widely used in Indonesia. Twitter users can engage in multiple activities, such as communicating with individuals and groups, writing daily activities, promoting businesses, arguing, and expressing ideas about a topic of discussion. At the beginning of June 2022, raising the entrance charge for Borobudur Temple became one of the concerns that caused a lot of conversation in the real world and on other social media platforms, including Twitter. The plan to increase the price of entrance tickets to Borobudur Temple has drawn various pro and con reactions in the community. This study analyzes public sentiment toward the planned increase in ticket prices for Borobudur Temple. Sentiment analysis of Twitter data can be implemented using a classification algorithm. The classification algorithms widely used in sentiment analysis research are Nave Bayes (NB) and Decision Tree (DT). The reason for choosing Nave Bayes and Decision Tree is because this algorithm is the most popular algorithm used to process text data classification; the process is simple, efficient, and performs well. This study's dataset source was taken from social media sites like Twitter. In comparison to the Decision Tree, which generates a test percentage of 100%, the accuracy of the Naive Bayes approach, based on the evaluation of the test results, produces the highest accuracy number. At the same time, the Decision Tree method's accuracy test yields a test accuracy value of 35.97%.
PENERAPAN METODE MULTI-KRITERIA FUZZY SYSTEM UNTUK MENGUKUR EFEKTIFITAS PADA BLOCKCHAIN PENDIDIKAN Kesuma, Martika; Chairani, Chairani
Jurnal informasi dan komputer Vol 12 No 01 (2024): Jurnal Informasi dan Komputer yang terbit pada tahun 2024 pada bulan 4 (April)
Publisher : LPPM Institut Teknologi Bisnis Dan Bahasa Dian Cipta Cendikia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35959/jik.v12i01.564

Abstract

Pendidikan mengalami perubahan yang signifikan akibat perkembangan teknologi, terutama selama pandemi COVID-19 yang memicu peningkatan penggunaan teknologi dalam pembelajaran online. Salah satu inovasi yang menarik perhatian adalah teknologi Blockchain, yang menawarkan alternatif dalam pengelolaan informasi dengan desentralisasi data. Namun, penerapan teknologi Blockchain dalam pendidikan masih terbatas, baik karena kurangnya pemahaman akan manfaatnya maupun karena fokus utama teknologi ini pada bidang keuangan dan kontrak. Meskipun demikian, beberapa lembaga pendidikan telah mulai memanfaatkan aplikasi Blockchain untuk verifikasi dan berbagi sertifikat akademik. Penelitian ini bertujuan untuk melakukan pemilihan platform tertbaik pada teknologi Blockchain dalam pendidikan dengan menggunakan Metode Multi-Kriteria Fuzzy System untuk mengukur efektivitasnya. Langkah-langkah metodologi melibatkan identifikasi dan tinjauan platform Blockchain yang tersedia, serta penggunaan Fuzzy Multi-Criteria Decision Analysis untuk memilih platform yang paling sesuai dalam pendidikan. Hasil penelitian menunjukkan bahwa platform Blockchain Quorum merupakan pilihan terbaik untuk pendidikan, dengan nilai Fuzzy MCDA tertinggi. Pada penggunaan metode Fuzzy MCDA memberikan landasan yang kuat dalam menentukan platform Blockchain terbaik untuk pendidikan. Penelitian ini menyoroti pentingnya eksplorasi teknologi Blockchain dalam meningkatkan efisiensi dan keamanan proses pendidikan, serta menawarkan wawasan yang berharga bagi pemangku kepentingan di bidang pendidikan dan teknologi.
Co-Authors Abdullah, Syaukani Adeng Slamet Aditya, Fiqri Akhmad Unggul Priantoro Ayu Zulfiani Azima, Muhammad Fauzan Belinda Analido Dedi Kundana Diki Andita Kusuma DWI SURYANTO Elpisah Fitria . Fitria . Fitria Fitria Harahap, Juliandi Hari Sabita Haris Luthfi, Muhammad Herwanto, Riko Kesuma, Martika Ketang Wiyono Kristyanto, Andi Lubis, Nita A. Luh Putu Ratna Sundari M Devid Alam Carnegie Novita, Eka Nur Listanto, Arif pratama, febri Rachma Zannati Rian Sefriadi Rifki, Rifki Arif Rio Kurniawan Rusika, Deddy Setiawan, Gatot B Sherly Trisnawati Siti Mukodimah Siti Nurhaliza Sri Suning Kusumawardani Sriyanto Sriyanto Sriyanto Sriyanto Sulyono Sunarso Sunarso Triloka, Joko Tupari Tupari Umar Zein W, Muhammad Fahreza Widyawan Yafik, Muhammad Ikrom Yuda Septiawan Yuni Arkhiansyah Yurnaliza, Yurnaliza