JOINCS (Journal of Informatics, Network, and Computer Science) JOINCS (Journal of Informatics, Network, and Computer Science) Universitas Muhammadiyah Sidoarjo en-US JOINCS (Journal of Informatics, Network, and Computer Science) 2541-5123 Facial Human Emotion Recognition by Using YOLO Faces Detection Algorithm <p>Deep emotions have gained importance recently because they constitute a form of interpersonal nonverbal communication that has been demonstrated and used in a variety of real-world contexts, including human-machine interactions, safety, and health. The best elements of a human face must be extracted in order to forecast the proper emotion expression, making this method extremely difficult. In this work, we provide a brand-new structural model to forecast human emotion on the face. The human face is found using the YOLO faces detection technique, and its attributes are extracted. These features then help to classify the face image into one of the seven emotions: natural, happy, sad, angry, surprised, fear, or disgust. The experiment demonstrated the robustness and speed of the suggested structure. This paper made use of the FER2013 dataset. The experimental findings demonstrated that the proposed system's accuracy was 94%.</p> Mustafa Asaad Hasan Copyright (c) 2023 Mustafa Asaad Hasan, Ali Hussein Lazem, Mohamed Ayad Alkhafaji, Hazeem B. Taher 2023-11-30 2023-11-30 6 2 32 38 10.21070/joincs.v6i2.1629 User And Bandwidth Management Using Mikrotik Hotspot At State Vocational School 5 Waingapu <p><em>The use of the internet in schools is very helpful for teachers, staff and students, the internet is an important need for most adult people, they even regard the internet as a basic daily need that must be met, such as at SMK Negeri 5 Waingapu. The use of the internet in schools is very helpful for teachers, staff and students. State Vocational High School 5 Waingapu uses an internet service provider (ISP) sourced from KOMINFO Bakti as internet access with a total bandwidth of 2 Mbps with an average user of 15 clients. The distance to access the internet network on WIFi is 20 meters, but there is still no good management of network usage so that when accessing the internet, many users (clients) often experience interference. One form of utilizing wireless technology is a hotspot. The development method used in this research is to develop a hotspot network at SMK Negeri 5 Waingapu using the Network Development Life Cycle (NDLC) model which is used as the overall development method in developing or designing computer network systems. By implementing bandwidth management using a hotspot router proxy at SMK Negeri 5 Waingapu and testing it using a speedtest before implementation, namely a download speed of 4.55 and an upload speed of 1.09. And after the implementation of bandwidth management for teacher user hotspots, namely for download bandwidth of 0.95 Mbps and upload bandwidth of 0.21 Mbps, then for hotspot user principals download bandwidth is 0.94 Mbps and upload bandwidth is 0.94 Mbps and hotspot user download bandwidth for students is 0.73 Mbps and upload of 0.94 Mbps.</em></p> Fajar Hariadi Deonisius Anamatalu Copyright (c) 2023 Deonisius Anamatalu, Fajar Hariadi 2023-11-30 2023-11-30 6 2 39 46 10.21070/joincs.v6i2.1605 Sarcasm Detection in News Headline Dataset with Ensemble Deep Learning Method <p><em>Sarcasm, a prevalent linguistic device, is frequently used in public discourse, often causing offence and distress to the listener. The complexity inherent in detecting sarcasm is a significant and ongoing challenge in the field of sentiment analysis research. The widespread use of this phenomenon in diverse conversational contexts further complicates its identification in data sets full of human interactions. Deficiencies in methodologies for distinguishing such statements adversely affect the performance of sentiment analysis, especially in distinguishing negative, positive or neutral sentiments. Inaccuracies in sarcasm detection can affect the classification results of sentiment analysis. Therefore, sentiment analysis seeks to categorise sarcastic sentences that, despite appearing positive, actually contain negative meanings. This research aims to build a deep learning ensemble stack model. The basic deep learning methods used are Bidirectional Gated Recurrent Unit (BiGRU) and Convolutional Neural Network (CNN). LightGBM is used to perform stack ensemble of deep learning methods. The dataset used comes from the Kaggle website and consists of English headlines. The findings show that the stack ensemble method outperforms BiGRU and CNN, evidenced by an accuracy rate of 91.2% and an F1 score of 90.2%. Therefore, from the above discussion, it can be concluded that the LightGBM method emerges as the optimal solution for sarcasm detection</em></p> Mochamad Alfan Rosid Siti Nur Haliza Yulian Findawati Uce Indahyanti Copyright (c) 2023 Mochamad Alfan Rosid, Siti Nur Haliza , Yulian Findawati, Uce Indahyanti 2023-11-30 2023-11-30 6 2 47 52 10.21070/joincs.v6i2.1628 Design Of An Expert System Using Bayes' Theorem Method For Web-Based Diagnosis Of Diseases In Caftage Animals <p><em>Kemajuan yang cepat pada teknologi informasi menjadikan teknologi sebagai kekuatan dalam berbagai bidang di era modern. Teknologi informasi khususnya pada bidang kecerdasan buatan telah membuat perangkat lunak sistem pakar. Sistem pakar adalah salah satu cabang ilmu dari kecerdasan buatan yang mengadopsi pengetahuan, fakta dan teknik penalaran pakar yang digunakan untuk memecahkan permasalahan yang biasanya hanya dapat dipecahkan oleh pakar dalam bidang tersebut, seperti&nbsp; pada peternak sapi di Balai Karantina Pertanian Kelas 1 Kupang, Wilayah Kerja Waingapu, terkadang sulit menemukan tenaga medis seperti dokter hewan ketika menemukan ternak sapi yang sakit. Salah satu bagian yang paling penting dalam penanganan kesehatan ternak adalah melakukan pengamatan terhadap ternak diduga sakit merupakan suatu proses untuk menentukan dan mengamati perubahan yang terjadi pada ternak melalui gejala-gejala yang diderita oleh sapi. Peternak sapi sering kali mengalami kendala dalam mengetahui penyakit sapi karena terbatasnya pengetahuan. Pada kondisi tersebut dibutuhkan peran seorang pakar dalam mengatasai penyakit pada sapi&nbsp; dengan menerapkan teorema bayes yang digunakan dalam statistika untuk menghitung peluang untuk suatu hipotesis. Metode yang digunakan yaitu metode waterfall dengan tahap analisis, desain, implementasi, dan pengujian. Metode pengumpulan data dilakukan dengan cara wawancara, observasi dan studi literatur. </em></p> Anastasiaa Ananggia Arini Aha Pekuwali Desy Asnath Sitaniapessy Copyright (c) 2023 Anastasiaa Ananggia, Arini Aha Pekuwali, Desy Asnath Sitaniapessy 2023-11-30 2023-11-30 6 2 53 64 10.21070/joincs.v6i2.1603 Learning Fun With Games Kahoot For Elementary School Students Grade 1 <p><em>Teaching and mentoring students in grade 1 SD is a big challenge for teachers. These constraints are felt by the teacher during the learning process. The difficulty in organizing students to follow and understand the lessons given so that not a few teachers use unique media so that students can be interested in the material that has been given. One example of manual learning media that teachers do in introducing letters, numbers, and objects is still using stickers or objects sold in the market. manual learning is good enough, but it does not rule out boredom and lack of responsiveness from students. The development of technology today is very good and has even become something that is very much needed, be it the use of social media, games, or other things. One of the features that kids usually like is Games. Therefore, I try to be able to combine the use of technology, especially learning media by using a laptop or headphones to make quizzes or games with the theme of learning using Kahoot to help teachers provide learning with unique media that students like and like. make students more interested and more creative in the learning process.</em></p> Hawu Yogia Pradana Uly Copyright (c) 2023 Hawu Yogia Pradana Uly 2023-11-30 2023-11-30 6 2 65 69 10.21070/joincs.v6i2.1600