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Sosialisasi dan Penerapan Aplikasi Sekolah Digital untuk Meningkatkan Produktifitas Kegiatan Akademik dan Keuangan Irfan Darmawan; Alam Rahmatulloh; Rohmat Gunawan; ZK Abdurahman Baizal; Albi Fitransyah
Surya Abdimas Vol. 7 No. 2 (2023)
Publisher : Universitas Muhammadiyah Purworejo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37729/abdimas.v7i2.2766

Abstract

Perkembangan teknologi informasi saat ini telah mempengaruhi berbagai sektor, salah satunya sektor pendidikan. Implementasi teknologi informasi di sektor pendidikan dapat dilakukan pada berbagai aspek diantaranya: administrasi sekolah dan pengelolaan keuangan. Berberapa masalah terkait administrasi sekolah dan pengelolaan keuangan diantaranya: pencatatan administrasi sekolah masih terpisah-pisah, sehingga terjadi redundansi data dan pengelolaan data yang kurang baik. Pencatatan administrasi keuangan masih dilakukan secara konvensional pada buku. Masih terdapatnya input data serupa yang berulang, pencarian data, pemeriksaan dan pelaporan keuangan membutuhkan waktu lama. Solusi untuk mengatasi permasalahan tersebut, pada kegiatan pengabdian ini dilakukan sosialisai dan pelatihan penggunaan aplikasi sekolah digital. Terdapat tiga tahapan utama yang dilakukan pada pengabdian ini: persiapan, pengembangan aplikasi, pelaksanaan. Aktivitas utama yang dilakukan pada saat pelaksanaan pengabdian diantaranya: sosialisasi aplikasi sekolah digital, demo fitur aplikasi sekolah digital, uji coba aplikasi sekolah digital, disktusi terkait aplikasi sekolah digital. Pengelolaan administrasi akademik dan keuangan merupakan fokus tahap pertama yang diimplementasi di lokasi mitra. Pengisian kuisioner oleh mitra dilakukan setelah kegiatan utama dilaksanakan. Respon mitra terhadap 6 pernyataan terkait pelaksaan kegiatan pengabdian, rata-rata memberikan nilai pada kategori S = Setuju, ini berarti mitra setuju dan mendukung terkadap kegiatan pengabdian ini.
Ontology-based Conversational Recommender System for Recommending Camera Restu Aditya Rachman; Z. K. A. Baizal
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 3 (2023): Juni 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i3.4852

Abstract

The camera is a product that has developed very quickly in terms of specifications and functions. In addition, the cameras available on the market are becoming increasingly varied, so customers need more time to find a camera that suits their needs. Currently, many recommender systems have been developed to assist users in finding suitable products, especially the conversational recommender system (CRS). CRS is a recommender system that recommends products through conversations between the user and the system. However, many developed CRS still forces users to have knowledge of the product's technical characteristics. In the real world, many people are not familiar with the technical features of products, especially cameras. People interact more easily with CRS by stating the camera function they want. In this study, we call that statement functional requirements. Therefore, we proposed a CRS for recommending cameras that interact with users using functional requirements. This CRS uses semantic reasoning techniques on ontologies. To evaluate system performance, we use two parameters, i.e., user satisfaction and recommendation accuracy. The evaluation results show that the accuracy of the recommendations is at a value of 82.35%, and the level of user satisfaction reaches 0.66. With these results, the system can provide recommendations accurately and satisfy users.
Group Recommender System using Matrix Factorization Technique for Book Domain Moh Naufal Mizan Saputro; Z. K. A. Baizal
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.v7i3.6435

Abstract

A recommender system helps users to select the desired items by analyzing the user's habit of interacting with the system. Recommender system also help the group of users for selecting items due to information overloads. Group Recommender System (GRS) is designed to identify all preferences within a group. An aggregation strategy is needed to accommodate all user preferences in a group. GRS is required in many cases, for example in the book domain, a bookstore recommends a list of books through a display for a group of visitors. We design a GRS for the book domain using Matrix Factorization technique. We utilize three methods to design GRS, such as After Factorization (AF), Before Factorization (BF), and Weighted Before Factorization (WBF). These three approaches were applied to three different group categories, i.e., small groups, medium groups, and large groups. We aim to find the best approach for each group category in this research. The evaluation metrics used are precision and recall in building this GRS. The results of this research indicate that a small group is suitable for using all three approaches, AF methods is the best approach methods for medium groups, and the best approach method for large groups is WBF.
Perancangan User Interface pada Aplikasi Gowes Berbasis Mobile Android Teguh Surya Apri Handoyo; Nungki Selviandro; Abdurahman Baizal
SEIKO : Journal of Management & Business Vol 6, No 2 (2023): July - December
Publisher : Program Pascasarjana STIE Amkop Makassar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37531/sejaman.v6i2.5027

Abstract

Gowes Virtual sebuah aplikasi yang dikembangkan tim project Wrap Entrpreneurship Universitas Telkom, target pengguna hanya baru mahasiswa maupun seseorang yang berkunjung ke Universitas Telkom. Aplikasi ini dibuat untuk Virtual Tour Universitas Telkom sambil berolahraga ketika terkendala dengan cuaca hujan ataupun ada rasa malas keluar ruangan. aplikasi ini disajikan berupa tampilan tiga Dimensi yang berlatar belakang Universitas Telkom maupun pemandangan kota-kota di Indonesia, agar lebih termotivasi lagi jika ingin bersepeda, pada perkembangan kedepannya ada berupa fitur Multiplayer dimana pengguna bisa bersepeda bersama. Oleh karena itu penulis merancang aplikasi tersebut dimulai dengan desain User Interface yang menarik dan mudah dipahami oleh pengguna, Pengujian ini sendiri menggunakan metode User Centered Design (UCD) untuk melakukan pendekatan terhadap calon pengguna. Dalam pengujian desain itu sendiri mengacu pada Single Ease Question (SEQ) merupakan salah satu Post Task Questionnaire yang digunakan dalam menilai tingkat kemudahan pada suatu fitur produk berdasarkan pengalaman user. Prototipe yang dibangun mendapatkan presentase pengujian skala 6 sebesar 58% dan skala rata-rata sebesar 6,36 yang mana memiliki tingkat kemudahan yang mudah. Hal ini melebihi skala yang telah ditentukan sebelumnya yaitu usability lebih besar sama dengan 5,6. Kata Kunci: User Interface, Gowes Virtual, User Centered Design,Universitas Telkom, Desain.
Movie Recommender System Using Decision Tree Method Muhammad Bilal Rafif Azaki; Z. K. A. Baizal
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 8, No 3 (2023)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v8i3.3867

Abstract

In this modern era, many things that can be done online, one of which is watching movies. When the number of movies increases, people often find it difficult to decide which movie to watch next. To solve this problem, a useful recommendation system was developed to find movies that one might like based on movies that have been watched before. This research develops a movie recommendation system using Collaborative Filtering (CF) with the Decision Tree algorithm. In this study, the data used were movie data and ratings obtained from the grouplens.org website. Then the movielens dataset is filtered and only saves movies with a rating of more than 50 that are used in the recommendation system. In this study, Mean Absolute Error (MAE) is used as a method to assess the accuracy of the movie recommendation system. Based on the research that has been done, Decision Tree gets better results with an MAE value of 0,942 compared to Collaborative Filtering with an MAE value of 1,242.
N-Days Tourist Route Recommender System in Yogyakarta Using Genetic Algorithm Method Muhammad Ridha Anshari; Z. K. A. Baizal
JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Vol 8, No 3 (2023)
Publisher : STKIP PGRI Tulungagung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29100/jipi.v8i3.3893

Abstract

Tourism is one of the proven solutions for the Indonesian economy. Tourism in certain regions, such as Yogyakarta, can significantly affect the region's economic development, including creating new jobs, creating new business opportunities, and increasing regional income. However, for tourists from outside Yogyakarta, it requires planning a tour before traveling in Yogyakarta, especially if he wants to spend several days on a tour. Many previous studies have developed systems that can recommend tourist routes, but not within a few days of tourist visits. In this study, we propose the use of Genetic Algorithm (GA) for automatically generating optimal travel itinerary for some days visit (n-days tour route). We develop the recommender system by combining GA and the concept of Multi-Attribute Utility Theory (MAUT). This MAUT used for accommodating user needs based some criteria such as rating, cost, and time. Based on our experimental results, GA is optimal in terms of execution time and number of attractions visited in n-days visit. The average execution time obtained is 59.62%, and the average number of attractions visited obtained is 45.95%. These results show that this method can generate tourist routes efficiently.
Book Recommender System Using Matrix Factorization with Alternating Least Square Method Hafid Ahmad Adyatma; Z. K. A. Baizal
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3816

Abstract

In this digital age, we are faced with countless choices of books. Finding books that match our interests and desires becomes a complex challenge. However, the existence of a book recommender system is useful to help provide the best decision-making experience that users can have. This research develops a book recommender system using Collaborative Filtering (CF) Matrix Factorization with Alternating Least Squares method which is compared with Singular Value Decomposition method to see an accurate recommender system. This research uses datasets from Goodreads in the form of book data and rating data. This research uses several evaluation metrics, namely RMSE and MAE for regression metrics and F1-Score and Precision for classification metrics. Based on the research that has been done, SVD gets a better accuracy value with an RMSE value of around 0.86822, for MAE values around 0.6903, for F1-Score values around 0.827923 and for Precision values around 0.568347. Meanwhile, the ALS algorithm gets an RMSE value of around 1.09320, for MAE value of around 0.86479, for F1-Score value of around 0.000304 and for Precision value of around 0.000596.
Chatbot-Based Book Recommender System Using Singular Value Decomposition Muhammad Attalariq; Z. K. A. Baizal
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3817

Abstract

In the era of information overload, finding the right book that matches one's preferences and interests has become a challenging task for users as many online book provider service websites such as Amazon, Goodreads, and Gramedia provide books of various types and choices. Recommender systems can be used in addressing such issues, it works by filtering information that provides predictions and suggests the best product or service to the user. Currently, various book recommender systems have been developed, but the systems do not provide interaction between the user and the system. Therefore, we propose a recommender system built with a conversational approach so that it can interact with natural language. Recommender system built using matrix factorization method with Singular Value Decomposition (SVD) algorithm, SVD is proven to have advantages for handling large datasets, extracting features, reducing noise and dimensionality so as to speed up computation. We performed two types of evaluation on the system. First, we tested the prediction accuracy using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) metrics. Second, we use questionnaires to measure user satisfaction levels. The evaluation of the system shows that the results of the prediction accuracy obtain an MAE value of 0.6481 and an RMSE value of 0.8287. Then, the accuracy performance of the system found that 83.2% of users get recommendations according to their interests. The user satisfaction with the whole system is 87.9%. The system built can provide a fairly good recommendation performance, and the chatbot can interact well with users based on the evaluation results obtained.
Ontology-Based Physical Exercise Recommender System for Underweight Using Ontology and Semantic Web Rule Language Christhofer Laurent Juliant; Z. K. A. Baizal; Ramanti Dharayani
Journal of Information System Research (JOSH) Vol 4 No 4 (2023): Juli 2023
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v4i4.3823

Abstract

Inactive lifestyles and unhealthy diets are often the result of people's busy lives. because of these bad habits, many people are underweight. diet and lack of physical activity are factors that cause underweight. Due to lack of information, people prefer to live lazily and not exercise. To solve this problem, we propose a physical exercise recommendation system that is explicitly designed for Indonesian people who are struggling with underweight. Despite the existence of various research studies advocating for physical activities tailored to individual preferences, there is currently no recommendation system available within a chatbot framework that includes a comprehensive session to be completed, along with specific sets and repetitions for each activity. This research proposes the utilization of ontology and Semantic Rule Web Language (SWRL) to represent and process the knowledge presented, enabling the development of rules for generating physical activity recommendations based on user preferences. By integrating the user profile, ontology, and the rules created, our system recommends physical exercise based on gender, weight, height, activity level, difficulty of movements, and the type of muscle to be trained. From the sample user data obtained, 408 physical exercises menu are recommended. The performance of the system is quite good, together with the validation results from personal trainers, obtained a precision value of 0.8, recall of 1, and f-score of 0.888. Concluding that the system we designed can provide physical activity recommendations in accordance with user preferences.
Effectiveness of Using Autoencoder Method on Recommender System in E-Commerce Domains Jayana Citra Agung Pramu Putra; Z K A Baizal
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.v7i3.6346

Abstract

The growth of large data in the online market can cause problems for users, one of which is in finding products that are to their liking. Recommender systems can overcome this problem by providing specific product recommendations to be promoted and offered to buyers, for example with Collaborative Filtering. The Collaborative Filtering Paradigm consists of Memory-based and Model-based techniques. Model-based techniques are considered to be able to complement the shortcomings of memory-based because of their high scalability, accuracy, and reduced dimensions. The type of model-based that is best known for having good results is Singular Value Decomposition (SVD) and what has been frequently used recently is deep learning, especially Autoencoder. The both models are very popular for use in dimension reduction so they are suitable for making recommendations. The advantage of Deep Learning is this method can be done without preprocessing so it can minimize the process that must be done. The evaluation results show that the errors produced by SVD and Autoencoder are lower compared to other studies. RMSE is 0.7 and MAE is 0.5. Even though the RMSE and MAE on the Autoencoder are greater than the SVD, the results of the T-Test show that there is no significant difference in the two error results. Autoencoders have been shown to have good results without preprocessing and are more effective with shorter processes and there are no significant differences with SVDs. Thus, Autoencoder can be said to be worthy of use and more effective in giving recommendations.
Co-Authors Abdul Muqit Abdullah Helmy Ade Kosasih Ade Romadhon Ade Sukma Adisti Rastosari Aditya, Naufal Adri Nur Fajari Afriani Sandra Agung Toto Wibowo Agus Alim Abdullah Ahmad Lubis Ghozali Akbar, Yoan Amri Alam Rahmatulloh Albi Fitransyah Ali, Muhammad Haidir Allismawita Allismawita amnah amnah An Fauzia Rozani Syafei Ana Fitriana Poerana Andiety, Rich Andini, Andini Andjioe, Oscar Rynandi Angelina Sagita Sastrawan Anindya, Widya Dara Aniq A Rahmawati Aniq A. Rahmawati Anisa Herdiani Annisa Cahya Anggraeni Annisa Cahya Anggraeni Antonius Randy Arjun Ardi Ardi Ari Satrio Arie Lasaprima Arifa Nur Hasanah Aryadi Pramarta Ayunda Farah Istiqamah Budiarti, L Endang Burhanuddin Bahar Cahya, Anindya Cahyani, Hilda Canda Ayu Arum Pertiwi Christhofer Laurent Juliant Cut Sri Maulina D. Novia Daffa Barin Tizard Riyadi Damayanti, Elok Dana Sulistyo Kusumo Danang Triantoro Murdiansyah Darmawan, Faiha Adzra Dede Tarwidi Dedi Romli Triputra Dendy Andra Deni Novia Dessy Abdullah Devi Pratami Devina Vanesa Dhiva Rezzy Pratama Diah Mahmuda Diah Pudi Langgeni Djoko Wahyono Donni Richasdy Dreyfus, Shoshana Dwi H Widyantoro Dwi Maya Sari Dwinda Tamara Edy Tandililing Eka Ismantohadi Elly Roza Elsa Rachel Dementieva Erbina Selvia Br Perangin-Angin Erliansyah Nasution Erni Masdupi Erwin B. Setiawan Erwin Budi Setiawan Esa Alfitrassalam Evitayani Evitayani Fadillah, Ichsan Alam Fatimah Nurhayani Fatimatus Zahroh Favian Dewanta Ferawati Ferawati Fernandy Marbun Ferry Lismanto Syaiful Firmansyah Firmansyah Fitriani Mangerangi Gentra Aditya Putra Ruswanda Gesit Tabrani Ghazi Ahmad Fadhlullah Gholib Gholib Grace Yohana Grace Yohana Gusti Ayu Marheni Gustina Lubis Hafid Ahmad Adyatma Hakim, Lukman Nur Hamlan andi Hary Yuswadi Hasanuddin Hasanuddin Hasanusi, Mohammad Helmi Arifin Hendra Naldi Hendri Andi Mesta Humaizi, Humaizi Humaizi, Humaizi Ichwanul Muslim Karo Karo Ida Ayu Putu Sri Widnyani Igga Febrian Virgiani Ika Arum Puspita Ilham Mujaddid Al Masyriq Imam Sunarno, Imam Ina Rofi’atun Nasihati Indira Adnani Indri Juliyarsi Inggrid Resmi Benita Intan Dwi Novieta, Intan Dwi Irfan Darmawan Irhas Jaya Iryanto Iryanto Iut Tri Utami Izzatul Ummah Jaka E. Sembodo Jamhari Jamhari Jamsari Jamsari Jaya, Irhas Jayana Citra Agung Pramu Putra Joni Dwi Pribadi Kalsum Kalsum Kemas M Lhaksmana Kemas M. Lhaksmana Kemas Muslim Lhaksmana Khaeruddin Yusuf Khaidarmansyah Khairiah, Khairiah Khamim, Khamim Khasrad . Khatimah, Ummu Husnul Khoirunnisaa’ Khoirunnisaa’ Khusnul Diana Kun Mustain Kusnadi, Kusnadi Lie Othman Lilis Suryani Lisa Rahmi Litasari Widyastuti, Litasari Liviandra, Monica Loiz, Andhika Lubis, Putri Handayani Lutfi Ambarwati M. Duskri M. Naufal Mu'afa M. Qadrian M. Rayhan Hakim M. Tahir Sapsuha Mahmud Imrona Mala Nurilmala Mansyur Arif Marayasa, I Nyoman Marendra Septianta Mayasari Mayasari Mella Ismelina F. Rahayu Miranti Andhita Scantya Mirna Fitrani Misna Ariani Mizanul Kirom Moch Arif Bijaksana Moh Naufal Mizan Saputro Moh Z Mubarok Moh. Mahsus Mudayatiningsih, Sri Muhamad Faishal Irawan Muhamad Hafidh Nofal Muhammad Adlim Muhammad Agus Muljanto Muhammad Alwi Nugraha Muhammad Attalariq Muhammad Bilal Rafif Azaki Muhammad Ilham Hafizha Muhammad Ilham Hafizha Muhammad Ridha Anshari Muhammad Zaid Dzulfikar Mustakim, ' Mustofa, Mutmainnah Mutmainnah Mustofa Najla Nur Adila Naufal Akbar Hartono Ni Nyoman Sumiasih Ni Wayan Armini Niken Titi Pratitis Ningsih Purba Ningsih, Ayu Oktavia Nirmala Ayu Aryanti Nisa, Intan Khairu Nofal, Muhamad Hafidh Nora. AN, Desri Nungki Selviandro Nur Azlina Nur Jamilah Nur Rahmawati Nur Ulfa Maulidevi Nuraini Lubis Nurjayanto, Bagus Wicaksono Nurul Ikhsan Okky Brillian Hibrianto Okky Brillian Hibrianto P, Kadek Abi Satria A V Pahrurrobi Pahrurrobi Paskalis Aditya Putra Prabowo, Ruh Devita Widhiana Prasetia, Reza Putra, A. D. A. Putu Harry Gunawan Qisti R Arvianti Rachmi Helfianur Radhiva Hibatullah, Muhammad Rafiuddin, Rafiuddin Rahmat Firdaus Rahmi Wati Rais Rais Ramadhan, Sageri Fikri Ramadhani, Nur Laili Ramanti Dharayani Randika Dwi Maulana Rasyid Ranestari Sastriani Rasbawati, Rasbawati Rayhan M Auliarahman Reinaldo Kenneth Darmawan Rena Feri Wijayanti Restu Aditya Rachman Reza Rendian Septiawan Rezano, Tomi Richo Fedhia Saldhi Rika Afriani Rina Dahlyanti Rinaldi Jasmi Rinita Amelia Risa Tiuria Risfaheri - Riska Padilah Riski Hernando Rita Rismala Rizaldy Arigi Rizky Andrian Rizqi Bayu Aji Robi Amizar Roby Dwi Hartanto Rohmat Gunawan Romy Adzani Adiputra Roseno, Rizky Haffiyan Rr. Amanda Pasca Rini, Rr. Amanda Pasca Rusli, Ridho Kurniawan S. Syamsurizal Sa'diatul Fuadiyah Sahlya Handayati Salam N. Aritonang Sanusi Ibrahim Sarini Vita Dewi Sedyo Mukti, Putri Ayu Sepri Reski Setiyoko, Didik Tri Shaufiah . Sigit Budisantoso Silvia Atika Anggrayni Simon He Siti Rohani Sitorus, Angela Tiara Maharani Solly Aryza Sri Andayani Sri Melia Suci Aprianti Sukanta Sumaryati Syukur Suyitman Suyitman Syaifuddin Ahrom Syaifuddin Ahrom Syaiful Akmal Syamsul Hadi Tebay, Selvi Teguh Surya Apri Handoyo Theriana Ayu Waskitaning Tyas Thoriq Akhdan, Muh Titi Sumanti Tongku Nizwan Siregar Ufra Neshia Umar Ali Ahmad Urnemi - Urnemi Urnemi Utomo, Muhajir Veritia, Veritia Vici E.H.S. Susilowati Wibowo, Kurnia Drajat Winardhi, Sonny Winardhi, Sonny Wiratama, Arga Kusuma Wiwik Handayani Wizna Wizna Wulandari, Dinda Atikah Yani Riyani Yanuar Firdaus Yanuar Firdaus A Yanuar Firdaus A.W. Yesi Chwenta Sari Yoan Amri Akbar Yolani Utami Yudha E. Pratama Yudha Endra Pratama Yuherman Yuherman Yulia Murni Yulia Yellita Yuliant Sibaroni Yulianti Fitri Kurnia Yuliawati Yuliawati Yulisna Gita Hapsari Yundari, Yundari Yusabri Yusran Khery, Yusran Yusri Dianne Jurnalis Yusza Reditya Murti Zidni Mubarok Zoni Hidayat