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All Journal Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Transmisi: Jurnal Ilmiah Teknik Elektro Jurnal Masyarakat Informatika Jurnal Simetris JAIS (Journal of Applied Intelligent System) Journal of Information System Sinkron : Jurnal dan Penelitian Teknik Informatika JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JOURNAL OF APPLIED INFORMATICS AND COMPUTING Dinamisia: Jurnal Pengabdian Kepada Masyarakat ILKOM Jurnal Ilmiah CARADDE: Jurnal Pengabdian Kepada Masyarakat IJISTECH (International Journal Of Information System & Technology) Building of Informatics, Technology and Science Infotekmesin International Journal of Engineering, Technology and Natural Sciences (IJETS) Abdimasku : Jurnal Pengabdian Masyarakat Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Teknik Informatika (JUTIF) IJISTECH Moneter : Jurnal Keuangan dan Perbankan Jurnal Informatika: Jurnal Pengembangan IT
Alzami, Farrikh
Faculty Of Computer Science, Universitas Dian Nuswantoro
Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Mechanical Engineering Social Sciences Other

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FUSION OF BAGGING BASED ENSEMBLE FRAMEWORK FOR EPILEPTIC SEIZURE CLASSIFICATION Alzami, Farrikh; Tamamy, Aries Jehan; Pramunendar, Ricardus Anggi; Arifin, Zaenal
Transmisi: Jurnal Ilmiah Teknik Elektro Vol 22, No 3 Juli (2020): TRANSMISI
Publisher : Departemen Teknik Elektro, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/transmisi.22.3.102-106

Abstract

The ensemble learning approach, especially in classification, has been widely carried out and is successful in many scopes, but unfortunately not many ensemble approaches are used for the detection and classification of epilepsy in biomedical terms. Compared to using a simple bagging ensemble framework, we propose a fusion bagging-based ensemble framework (FBEF) that uses 3 weak learners in each oracle, using fusion rules, a weak learner will give results as predictors of the oracle. All oracle predictors will be included in the trust factor to get a better prediction and classification. Compared to traditional Ensemble bagging and single learner type Ensemble bagging, our framework outperforms similar research in relation to the epileptic seizure classification as 98.11±0.68 and several real-world datasets
Document Preprocessing with TF-IDF to Improve the Polarity Classification Performance of Unstructured Sentiment Analysis Alzami, Farrikh; Udayanti, Erika Devi; Prabowo, Dwi Puji; Megantara, Rama Aria
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 5, No. 3, August 2020
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v5i3.1066

Abstract

Sentiment analysis in terms of polarity classification is very important in everyday life, with the existence of polarity, many people can find out whether the respected document has positive or negative sentiment so that it can help in choosing and making decisions. Sentiment analysis usually done manually. Therefore, an automatic sentiment analysis classification process is needed. However, it is rare to find studies that discuss extraction features and which learning models are suitable for unstructured sentiment analysis types with the Amazon food review case. This research explores some extraction features such as Word Bags, TF-IDF, Word2Vector, as well as a combination of TF-IDF and Word2Vector with several machine learning models such as Random Forest, SVM, KNN and Naïve Bayes to find out a combination of feature extraction and learning models that can help add variety to the analysis of polarity sentiments. By assisting with document preparation such as html tags and punctuation and special characters, using snowball stemming, TF-IDF results obtained with SVM are suitable for obtaining a polarity classification in unstructured sentiment analysis for the case of Amazon food review with a performance result of 87,3 percent.
Developing The Concepts & Strategy of Smart Regional: How to Increase Tourism & Investors (Smart city 4.0) Sendi Novianto; Farrikh Alzami; Indra Gamayanto
IJISTECH (International Journal of Information System and Technology) Vol 5, No 3 (2021): October
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/ijistech.v5i3.140

Abstract

Smart city 4.0 is a development of the three previously published articles, namely smart city 1.0,2.0 and 3.0. The smart city article that we created is a long process in research, so we have to make it gradually and make it into several parts. Smart city 1.0 and 2.0 we designed a big picture of a smart city and how the maturity level understood. Next, in smart city 3.0, our focus is on how smart education can be applied in collaboration between each educational institution so that a high-quality level of education achieved in the area. In smart city 4.0, we focus on designing a concept that focuses on developing tourists and how to get investors interested in investing. Factors such as culture, infrastructure/facilities, human resource development in the community, safety and comfort, profit sharing, development of tourist attractions, innovation in improving people's lives and many other factors are our main concern in this article. The methods used in smart city 4.0 are PDCA & USEPDASA, which are both appropriate methods for developing a special framework for tourists and investors. The stages in smart city 4.0 are mapping in terms of culture, human resources, facilities, information technology, security and comfort. These divided into two parts: on the side of increasing tourists and the side of increasing investors. The result of smart city 4.0 is a framework to be able to connect all activities in a smart city to generate profits for the area.
Designing The Concepts - Framework & The Maturity Level Of Smart Pondok Pesantren (Islamic Boarding Schools) Sendi Novianto; Indra Gamayanto; Farrikh Alzami
Journal of Applied Intelligent System Vol 6, No 2 (2021): Journal of Applied Intelligent System
Publisher : Universitas Dian Nuswantoro and IndoCEISS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33633/jais.v6i2.4602

Abstract

Abstract - The development of information technology and significant progress is happening today, where changes need to face increasingly high global competition. Islamic boarding schools are the centre of change, where boarding schools must develop four important factors: human resource development, information technology, culture and e-spirituality. This article is about developing smart city 3.0 and 5.0, where the resulting framework and methods can apply to Islamic boarding schools. Our research object is Islamic boarding schools, and in this place, we do community service. This article's results are the framework and the maturity level of smart Islamic boarding schools, where there are three indicators (people-culture, technology-business, implementation-global goals)  and three important levels (stage 1-the foundation, stage 2-good foundation, stage 3- great foundation), which are the basis for developing a smart islamic boarding school. Moreover, this article is version 1.0, which in its development will extend to the application. An application Si-PenO (Online registration information system) completed at the Askhabul Kahfi Islamic boarding school as proof of applying the concept we created in this article. This change is needed to face globalization, and change is a good thing if it does systematically. Furthermore, this article will continue to develop in versions 2.0 and 3.0 and applications that we will implement in Islamic boarding schools.
Employee Attrition and Performance Prediction using Univariate ROC feature selection and Random Forest Aris Nurhindarto; Esa Wahyu Andriansyah; Farrikh Alzami; Purwanto Purwanto; Moch Arief Soeleman; Dwi Puji Prabowo
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 6, No. 4, November 2021
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v6i4.1345

Abstract

Each company applies a contract extension to assess the performance of its employees. Employees with good performance in the company are entitled to future contracts within a certain period of time. In a pandemic time, many companies have made decisions to carry out WFH (Work from Home) activities even to Termination (Attrition) of Employment. The company's performance cannot be stable if in certain fields it does not meet the criteria required by the company. Thus, due to many things to consider in contract extension, we are proposed feature selection steps such as duplicate features, correlated features and Univariate Receiver Operating Characteristics curve (ROC) to reduce features from 35 to 21 Features. Then, after we obtained the best features, we applied into Decision Trees and Random Forest. By optimizing parameter selection using parameter grid, the research concluded that Random Forest with feature selection can predict Employee Attrition and Performance by obtain accuracy 79.16%, Recall 76% and Precision 82,6%. Thus with those result, we can conclude that we can obtain better prediction using 21 features for employee attrition and performance which help the higher management in making decisions.
Analisis Sentimen Masyarakat Terhadap Layanan Shopeefood Melalui Media Sosial Twitter Dengan Algoritma Naïve Bayes Classifier Farah Syadza Mufidah; Sri Winarno; Farrikh Alzami; Erika Devi Udayanti; Ramadhan Rakhmat Sani
JOINS (Journal of Information System) Vol 7, No 1 (2022): Edisi Mei 2022
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1882.622 KB) | DOI: 10.33633/joins.v7i1.5883

Abstract

Twitter adalah salah satu media sosial dan fasilitas microblogging yang menjadi tempat bagi penggunanya berbagi pengalamannya secara bebas, realtime, dan bersifat publik. Hal ini dapat menjadikan twitter sebagai sumber informasi yang dapat berupa opini, ataupun komentar yang bersifat positif maupun negatif. Dari opini masyarakat tersebut dapat diimplementasikan sebagai tolak ukur, karena memiliki nilai bagi suatu perusahaan agar dapat menjadi bahan evaluasi untuk menentukan langkah dalam meningkatkan layanannya. Oleh karena itu untuk mengolah opini tersebut dibutuhkan teknik analisis sentimen untuk dapat mengidentifikasi opini baik positif maupun negatif. Pada penelitian ini akan menganalisis tweet berbahasa Indonesia dengan topik layanan yang ada pada E-commerce shopee yaitu layanan ShopeeFood yang sedang populer dikalangan masyarakat saat ini. Metode yang akan digunakan untuk analisa sentimen pada penelitian ini yaitu Naïve Bayes Classifier untuk proses mengklasifikasi. Berdasarkan hasil pengujian klasifikasi tweet pada penelitian ini dibuktikan keakuratan yang didapatkan melalui confusion matrix dengan nilai accuracy sebesar 90,62%, precision sebesar 88,23%, dan recall sebesar 93,75%. Kata kunci: Analisis Sentimen, Teks Mining, Naïve Bayes Classifier, Twitter, ShopeeFood
Sentiment Analysis of Community Response Indonesia Against Covid-19 on Twitter Based on Negation Handling Viry Puspaning Ramadhan; Purwanto Purwanto; Farrikh Alzami
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 7, No. 2, May 2022
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v7i2.1429

Abstract

The use of the internet globally, especially on the use of social media, includes Indonesia as one of the most active users in the world. The amount of information that can be obtained can be used to be processed into useful information, for example, information about the public sentiment on a particular topic. Tracking and analyzing tweets can be a method to find out people's thoughts, behavior, and reactions regarding the impact of Covid-19. The key to sentiment analysis is the determination of polarity, which determines whether the sentiment is positive or negative. The word negation in a sentence can change the polarity of the sentence so that if it is not handled properly it will affect the performance of the sentiment classification. In this study, the implementation of negation handling on sentiment analysis of Indonesian people's opinions regarding COVID-19 on Twitter has proven to be good enough to improve the performance of the classifier. Accuracy results obtained are 59.6% compared to adding negation handling accuracy obtained is 59.1%. Although the percentage result is not high, documents that include negative sentences have more meaning than negative sentences. However, for the evaluation using the MCC evaluation matrix, the results were quite good for the testing data. For the results of the proposed method whether it is suitable for data that has two classes or three classes when viewed from the results of the evaluation matrix, the proposed method is more suitable for binary data or data that has only two classes.
Developing The Concepts & Strategy of Smart Regional: How to Increase Tourism & Investors (Smart city 4.0) Sendi Novianto; Farrikh Alzami; Indra Gamayanto
IJISTECH (International Journal of Information System and Technology) Vol 5, No 3 (2021): October
Publisher : Sekolah Tinggi Ilmu Komputer (STIKOM) Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (638.493 KB) | DOI: 10.30645/ijistech.v5i3.140

Abstract

Smart city 4.0 is a development of the three previously published articles, namely smart city 1.0,2.0 and 3.0. The smart city article that we created is a long process in research, so we have to make it gradually and make it into several parts. Smart city 1.0 and 2.0 we designed a big picture of a smart city and how the maturity level understood. Next, in smart city 3.0, our focus is on how smart education can be applied in collaboration between each educational institution so that a high-quality level of education achieved in the area. In smart city 4.0, we focus on designing a concept that focuses on developing tourists and how to get investors interested in investing. Factors such as culture, infrastructure/facilities, human resource development in the community, safety and comfort, profit sharing, development of tourist attractions, innovation in improving people's lives and many other factors are our main concern in this article. The methods used in smart city 4.0 are PDCA & USEPDASA, which are both appropriate methods for developing a special framework for tourists and investors. The stages in smart city 4.0 are mapping in terms of culture, human resources, facilities, information technology, security and comfort. These divided into two parts: on the side of increasing tourists and the side of increasing investors. The result of smart city 4.0 is a framework to be able to connect all activities in a smart city to generate profits for the area.
Implementation Of ETL E-Commerce For Customer Clustering Using RFM And K-Means Clustering Farrikh Alzami; Fikri Diva Sambasri; Rifqi Mulya Kiswanto; Rama Aria Megantara; Ahmad Akrom; Ricardus Anggi Pramunendar; Dwi Puji Prabowo; Puri Sulistiyawati
Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi) Vol 10 No 3 (2022): Vol. 10, No. 3, December 2022
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/JIM.2022.v10.i03.p05

Abstract

E-commerce is the activity of selling and buying goods through an online system or online. One of the business models in which consumers sell products to other consumers is the Customer to Customer (C2C) business model. One of the things that need to be considered in this business model is knowing the level of customer loyalty. By knowing the level of customer loyalty, the company can provide several different treatments to its customers so that they can maintain good relations with customers and can increase product purchase revenue. In this study, the author wants to segment customers on data in E-commerce companies in Brazil using the K-Means clustering algorithm using the RFM (Recency, Frequency, Monetary) feature. There are also several ETL stages of research that must be carried out, namely taking data from the open public data site (Kaggle), which consist of more than 9 tables (extract), then merging the data to select some data that needs to be used (transform and load), understanding data by displaying it in graphic form, conducting data selection to select features / attributes. which is in accordance with the proposed method, performs data preprocessing, and creates a model to get the cluster. Based on the results of the research that has been done, the number of clusters is 4 clusters with the evaluation value of the model using the silhouette score is 0.470.
Red Onion Customer Relationship Management System Business Process Design Using BPR LC Method Aditya Rahman; Ika Novita Dewi; Farrikh Alzami; Kukuh Biyantama; Muhammad Rizal Nurcahyo; Filmada Ocky Saputra; Rindra Yusianto; Mila Sartika; Firman Wahyudi
JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING Vol. 6 No. 2 (2023): Issues January 2023
Publisher : Universitas Medan Area

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31289/jite.v6i2.8525

Abstract

Customer Relationship Management merupakan sistem yang membantu proses bisnis dalam mengelola hubungan antara perusahaan atau organisasi dengan pelanggan. Akan tetapi Customer Relationship (CRM) jarang ditemui dalam sektor pertanian, terutama pada pertanian bawang merah di Jawa Tengah. Penelitian ini bertujuan untuk merekayasa ulang dan memperbarui proses bisnis yang sedang berjalan guna memperbaiki permasalahan tersebut dengan menggunakan teknologi Machine Learning dan memodelkan proses bisnis dengan Business Procces Modeling Notation (BPMN). Untuk memperlancar tujuan penelitan, penelitian ini menggunakan metode Business Process Reengineering Life Cycle untuk menghasilkan CRM bawang merah. Pada penelitian ini menghasilkan sebuah temuan yaitu proses bisnis yang baru dengan menyertakan teknologi Machine Learning yang ditampung pada aplikasi cluster petani yang telah digambarkan pada BPMN, hal tersebut dilakukan agar menunjang kekurangan dalam kegiatan petani agar lebih menjadi efisien dan optimal serta mendapatkan hasil panen yang diinginkan.
Co-Authors Abu Salam Aditya Rahman Adriani, Mira Riezky Ahmad Akrom Ahmad Akrom Ahmad Khotibul Umam, Ahmad Khotibul Ahmad Zainul Fanani Ahmad Zaniul Fanani Akrom, Ahmad Al-Azies, Harun Alpiana, Vika Alvin Steven Arifin, Zaenal Aris Nurhindarto Ashari, Ayu Asih Rohmani, Asih Azzami, Salman Yuris Adila Budi, Setyo Candra Irawan Candra Irawan Caturkusuma, Resha Meiranadi Chaerul Umam Chaerul Umam Chaerul Umam Chaerul Umam Choirinnisa, Dina Dewi Agustini Santoso Diana Aqmala Dwi Puji Prabowo Dwi Puji Prabowo Dwi Puji Prabowo, Dwi Puji Enrico Irawan Erika Devi Udayanti Esa Wahyu Andriansyah Fahmi Amiq Farah Syadza Mufidah Fikri Diva Sambasri Fikri Diva Sambasri Fikri Firdaus Tananto Fikri Firdaus Tananto Filmada Ocky Saputra Filmada Ocky Saputra Firman Wahyudi Firman Wahyudi Firman Wahyudi, Firman Fitri Susanti Ghina Anggun Hadi, Heru Pramono Hartono, Andhika Rhaifahrizal Harun Al Azies Hasan Aminda Syafrudin Ifan Rizqa Ika Novita Dewi Ika Novita Dewi Indra Gamayanto Indra Gamayanto Indrayani, Heni ISWAHYUDI ISWAHYUDI Jumanto Karin, Tan Regina Khariroh, Shofiyatul Khoirunnisa, Emila Krisnawati, Dyah Ika Kukuh Biyantama Kukuh Biyantama Kusmiyati Kusmiyati Kusmiyati Kusmiyati* Kusumawati, Yupie L. Budi Handoko Lalang Erawan Lesmarna, Salsabila Putri Mahmud Mahmud Marjuni, Aris Megantara, Rama Aria Mila Sartika Mila Sartika, Mila Mira Nabila Mira Nabila Moch Arief Soeleman Moh Hadi Subowo Moh. Yusuf, Moh. Muhammad Naufal, Muhammad Muhammad Noufal Baihaqi Muhammad Ridho Abdillah Muhammad Riza Noor Saputra Muhammad Rizal Nurcahyo Muslich Muslich, Muslich Muslih Muslih MY. Teguh Sulistyono Nuanza Purinsyira Nugraini, Siti Hadiati Nurhindarto, Aris Nurhindarto, Aris Pergiwati, Dewi Pratiwi, Yunita Ayu Pulung Nurtantio Andono Pulung Nurtantyo Andono Puri Sulistiyawati Puri Sulistiyawati Puri Sulistiyawati Purwanto Purwanto Purwanto Purwanto Puspitarini, Ika Dewi Rama Aria Megantara Rama Aria Megantara Ramadhan Rakhmat Sani Ricardus Anggi Pramunendar Rifqi Mulya Kiswanto Rini Anggraeni Risky Yuniar Rahmadieni Ritzkal, Ritzkal Rohman, M. Hilma Minanur Ruri Suko Basuki Saputra, Filmada Ocky Saputra, Resha Mahardhika Saputri, Pungky Nabella Sasono Wibowo Sejati, Priska Trisna Sendi Novianto Sendi Novianto sigit muryanto Sinaga, Daurat Soeleman, Arief Sri Handayani Sri Winarno Sri Winarno Steven, Alvin Subowo, Moh Hadi Sukamto, Titien Suhartini Sulistiyono, MY Teguh Sulistyowati, Tinuk Sutriawan Sutriawan Tamamy, Aries Jehan Thifaal, Nisrina Salwa Viry Puspaning Ramadhan Wellia Shinta Sari Wibowo, Isro' Rizky Widodo Yusianto Rindra Yuventius Tyas Catur Pramudi Zaenal Arifin Zahro, Azzula Cerliana Zulfiningrumi, Rahmawati