Garuda - Garba Rujukan Digital

Article Per Year (5 Year)

All

Journal of Software Engineering, Information and Communication Technology

SEICT
Website

Published by Universitas Pendidikan Indonesia

ISSN : 27741656 EISSN : 27741699 DOI : https://doi.org/10.17509/seict

Core Subject : Science, Education, Engineering,

Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Engineering

The Journal of Software Engineering, Information and Communication Technology promotes research in the broad field of science and technology (including such disciplines as Agriculture, Environmental Science, etc.) with particular respect to Indonesia, but not limited to authorship or topical coverage within the region. Contributions are expected from senior researchers, project managers, research administrators and PhD students at advanced stages of their research, representing both public organizations and private industry. Equally, the journal if intended for scholars and students, reseachers working at research organizations and government agencies, and also for enterprises undertaking applied R&D to lead innovations. The editorial contents and elements that comprise the journal include: Theoretical articles Empirical studies Practice-oriented papers Case studies Review of papers, books, and resources. As far as the criteria for evaluating and accepting submissions is concerned, a rigorous review process will be used. Submitted papers will, prior to the formal review, be screened so as to ensure their suitability and adequacy to the journal. In addition, an initial quality control will be performed, so as to ensure matters such as language, style of references and others, comply with the journals style. Focus And Scope Software engineering Information technology Data Science AI/ML Cloud Computing, Big Data and Social Computing Image Processing Applied Informatics Database Technologies and Applications Digital Information Computation and Retrieval Information Security Human Computer Interaction Multimedia and Game Data Mining Ubiquitous Computing Business Intelligence and Knowledge Management Iot Software Engineering Education

Arjuna Subject : Ilmu Komputer (semua kategori) - Perangkat Lunak

Articles 38 Documents

1 2 3 4

IMPLEMENTASI DEEP LEARNING PADA SIMULASI AUTONOMOUS DRIVE MENGGUNAKAN AIRSIM Violla Gunova
Journal of Software Engineering, Information and Communication Technology (SEICT) Vol 1, No 1: December 2020
Publisher : Universitas Pendidikan Indonesia (UPI)

Autonomous Driving is one of the technologies available in Artificial Intelligence. This technology aims to facilitate human work related to driving. The application of this technology can be done by using one of the main capabilities found in Artificial Intelligence, namely Machine Learning. By using several methods found in Machine Learning, Autonomous Driving Technology can be formed and developed. This paper will discuss how to make Autonomous Driving technology using available methods and show the results obtained from using these methods.

Twenty First Century Skill Attainment Using Creative Learning Cycle Method with Learning Management System Media (A Case Study of Basic Programming Subject in 11th Grade Computer and Networking Study SMKN 1 Cimahi) Asyifa Imanda Septiana
Journal of Software Engineering, Information and Communication Technology (SEICT) Vol 2, No 1: June 2021
Publisher : Universitas Pendidikan Indonesia (UPI)

Twenty first century skills is a set of skills that has to be mastered by students in order to win new era competitions where the competitions are started to open globally. Several researches have shown that Creative Learning Cycle method can help students to master the twenty first century skills and make learning process more meaningful. The purpose of this research is to identify the influence of Creative Learning Cycle learning steps to the students’ twenty first century skills. Another purpose of the research is to make a learning management system (LMS) that might enhance learning process done with Creative Learning method. Aside to manage the learning process, LMS can be used as twenty first century skills evaluation media in each step because it has been integrated with twenty first century learning parameters according to the analysis that has been done before. Even though in the end the LMS that has been built cannot accommodate all Creative Learning Cycle learning steps, LMS still get positive response from the teacher and students who use it

Klasifikasi Komentar Video Instruksional Populer Bertemakan Pekarangan Perkotaan menggunakan Auto-Keras Trisna Gelar; Aprianti Nanda Sari
Journal of Software Engineering, Information and Communication Technology (SEICT) Vol 1, No 1: December 2020
Publisher : Universitas Pendidikan Indonesia (UPI)

Keterbatasan kompetensi menjadi halangan untuk memulai melakukan kegiatan pekarangan perkotaan. Mempraktikkan langkah-langkah pada video instruksional populer di Youtube dari individu maupun profesional dapat meningkatkan kompetensi diri. Namun, kualitas video instruksional(konten, audio dan visual) sangat bervariasi bergantung pada orang yang memproduksinya. Penonton secara langsung dapat berinteraksi dengan memberikan apresiasi (positif maupun negatif), tanggapan atau pertanyaan pada kolom komentar seputar topik yang dipresentasikan. Umpan balik tersebut digunakan untuk memperbaiki kualitas dari video seperti memberikan penjelasan mendalam untuk topik yang sering ditanyakan dan melanjutkan atau menghentikan video berdasarkan topik yang paling disukai atau sebaliknya. Pekerjaan klasifikasi komentar dapat diselesaikan dengan mudah menggunakan Auto-Keras karena proses pemilihan model, pencarian arsitektur neural-network dan evaluasi model terbaik dilakukan secara otomatis. Penelitian pada umumnya terdiri atas empat fase, yaitu (1) pengumpulan dataset, (2) text processing, (3) feature engineering, dan (4) pemodelan dan evaluasi. Pada penelitian ini telah terkumpul 5194 komentar berlabel(aspirasi, pertanyaan, dan pernyataan) dari 5 video instruksional populer bertemakan pekarangan kota yang dikurasi oleh penulis berdasarkan urutan views, likes dan dislikes tertinggi. Kualitas kalimat komentar diperbaiki pada fase persiapan melalui proses text cleaning, normalization, tokenization dan stemming. Pada proses normalization, kamus istilah pertanian menjadi informasi agar tidak tercampur dengan bahasa informal yang mirip. Kalimat komentar yang telah normal dikonversikan menjadi n-gram dan word embedding sebagai input auto-keras. Dari hasil pengujian evaluasi model, akurasi yang dihasilkan auto-keras dengan fitur word embedding mencapai 86.91% sedikit lebih baik dari akurasi fitur n-gram 86.33%.Keterbatasan kompetensi menjadi halangan untuk memulai melakukan kegiatan pekarangan perkotaan. Mempraktikkan langkah-langkah pada video instruksional populer di Youtube dari individu maupun profesional dapat meningkatkan kompetensi diri. Namun, kualitas video instruksional(konten, audio dan visual) sangat bervariasi bergantung pada orang yang memproduksinya. Penonton secara langsung dapat berinteraksi dengan memberikan apresiasi (positif maupun negatif), tanggapan atau pertanyaan pada kolom komentar seputar topik yang dipresentasikan. Umpan balik tersebut digunakan untuk memperbaiki kualitas dari video seperti memberikan penjelasan mendalam untuk topik yang sering ditanyakan dan melanjutkan atau menghentikan video berdasarkan topik yang paling disukai atau sebaliknya. Pekerjaan klasifikasi komentar dapat diselesaikan dengan mudah menggunakan Auto-Keras karena proses pemilihan model, pencarian arsitektur neural-network dan evaluasi model terbaik dilakukan secara otomatis. Penelitian pada umumnya terdiri atas empat fase, yaitu (1) pengumpulan dataset, (2) text processing, (3) feature engineering, dan (4) pemodelan dan evaluasi. Pada penelitian ini telah terkumpul 5194 komentar berlabel(aspirasi, pertanyaan, dan pernyataan) dari 5 video instruksional populer bertemakan pekarangan kota yang dikurasi oleh penulis berdasarkan urutan views, likes dan dislikes tertinggi. Kualitas kalimat komentar diperbaiki pada fase persiapan melalui proses text cleaning, normalization, tokenization dan stemming. Pada proses normalization, kamus istilah pertanian menjadi informasi agar tidak tercampur dengan bahasa informal yang mirip. Kalimat komentar yang telah normal dikonversikan menjadi n-gram dan word embedding sebagai input auto-keras. Dari hasil pengujian evaluasi model, akurasi yang dihasilkan auto-keras dengan fitur word embedding mencapai 86.91% sedikit lebih baik dari akurasi fitur n-gram 86.33%.

Prediksi Penyebaran Demam Berdarah Dangue dengan Algoritma Hybrid Autoregressive Integrated Moving Average dan Artificial Neural Network: Studi Kasus di Kabupaten Bandung Ichwanul Muslim Karo Karo
Journal of Software Engineering, Information and Communication Technology (SEICT) Vol 2, No 1: June 2021
Publisher : Universitas Pendidikan Indonesia (UPI)

Demam Berdarah Dengue (DBD) merupakan penyakit menular yang ditularkan melalui gigitan nyamuk Aedes Aegypti. WHO (World Health Organization) telah mengupayakan langkah-langkah pencegahan terhadap wabah DBD dengan penerapan teknologi. Teknologi yang digunakan untuk mencegah penyebaran wabah DBD adalah penggunaan serangkaian proses komputasi untuk menghasilkan prediksi penyebaran DBD yang diharapkan dapat membantu langkah pencegahan. Dalam membantu pengembangan teknologi pencegahan DBD penulis mengembangkan model hybrid Autoregressive Integrated Moving Average (ARIMA) dan Artificial Neural Network (ANN) untuk membantu memprediksi incident rate DBD berdasarkan beberapa variabel terkait seperti cuaca dan incident rate yang diambil dari Januari 2009 – November 2016. Dari model hybrid ARIMA dan ANN dihasilkan nilai prediksi yang memiliki tingkat error yang rendah yang diindikasikan oleh nilai RMSE yang kecil. Model hybrid ARIMA-ANN yang optimal adalah hybrid ARIMA-ANN dengan orde (1,0,3) dengan nilai RMSE sebesar 0.0087

Application of Deep Learning using Convolutional Neural Network (CNN) Algorithm for Gesture Recognition Ahmad Abuzar Alhamdani
Journal of Software Engineering, Information and Communication Technology (SEICT) Vol 4, No 1: June 2023
Publisher : Universitas Pendidikan Indonesia (UPI)

Gesture recognition is a fascinating method of human-computer interaction that goes beyond traditional means such as keyboards, pointers, and joypads. In gesture recognition, Convolutional Neural Network (CNN) algorithms are utilized in Deep Learning to train models using datasets comprising gesture images. The training process involves pattern recognition and identification of crucial features from gesture images, followed by evaluation to measure the model's accuracy. Gesture recognition holds immense potential across various fields, including human-computer interaction, gaming, healthcare, and autonomous vehicles, and continues to be a focus of research and development in the future.

Analisis User Interface Terhadap Website Badan Pusat Statistik Kota Balikpapan Dengan Menggunakan Metode Heuristic Evaluation Ridha Auliya; Sri Rahayu Natasia; Intan Wahyu Nur Rachma; Mahiza Imam Ma’arif; Maulidhiyah Faizah; Moch. Fattah Ibnu Azmi
Journal of Software Engineering, Information and Communication Technology (SEICT) Vol 2, No 1: June 2021
Publisher : Universitas Pendidikan Indonesia (UPI)

Badan Pusat Statistik adalah Lembaga Pemerintah Non Kementerian yang bertanggung jawab langsung kepada Presiden. Website Badan Pusat Kota Balikpapan menyediakan data statistic berkualitas untuk Indonesia maju. Tentunya website Badan Pusat Statistik Kota Balikpapan menjadi salah satu interaksi bagi penggunanya. Beberapa fitur-fitur dari website Badan Pusat Statistik Kota Balikpapan yang ditampilkan seperti berita, informasi kependudukan, informasi ekonomi dan masih banyak fitur yang berguna bagi pengguna. Unstuck melakukan analisis terhadap website Badan Pusat Statistik Kota Balikpapan, penelitian ini menggunakan metode Heuristic Evaluation. Permasalahan terhadap user interface yang ada pada website tersebut ialah fitur pencarian yang sulit digunakan dan rumit karena harus menggunakan captcha. Selain itu, saat mengubah tampilan website menjadi berbahasa inggris, terdapat data informasi terbaru yang tidak ditampilkan dan tampilan user interface yang kurang efisien seperti navigasi yang kurang rapi, fitur ekspansi sidebar dengan 2 tombol yang memiliki fungsi sama, terdapat media gambar yang tidak muncul pada tampilan, dan beberapa dokumen publikasi yang tidak dapat diunduh. Hal ini membuat pengguna merasa kurang nyaman dengan user interface pada website balikpapankota.bps.go.id. Penelitian ini memperoleh tingkat permasalahan pengguna berdasarkan severity rating pada heuristic evaluation, sehingga menghasilkan sebanyak 4 rekomendasi desain tampilan perbaikan untuk desktop website balikpapankota.bps.go.id, dan 6 rekomendasi desain tampilan perbaikan untuk mobile website balikpapankota.bps.go.id dan rekomendasi perbaikan tampilan berupa rancangan mockup dan prototype yang telah dievaluasi oleh kelompok atau evaluator untuk dijadikan sebagai acuan referensi dalam melakukan proses redesign website balikpapankota.bps.go.id

Implementasi Metode XGBoost dan Feature Important untuk Klasifikasi pada Kebakaran Hutan dan Lahan Ichwanul Muslim Karo Karo
Journal of Software Engineering, Information and Communication Technology (SEICT) Vol 1, No 1: December 2020
Publisher : Universitas Pendidikan Indonesia (UPI)

Kebakaran hutan dan lahan di Indonesia telah menjadi masalah krisis lingkungan tahunan. Sebaran kebakaran hutan terbesar terjadi dipulau Sumatera. Salah satu upaya tindakan dalam pencegahan dan meminimalisasikan resiko kebakaran hutan adalah dengan mengklasifikasikan jenis titik panas di lahan, sehingga di dapat skala prioritas dalam pemadaman titik api. Penelitian ini bertujuan mengklasifikasikan type titik panas dengan metode XGBoost dan feature importance yang terdapat di pulau Sumatera. Data titik panas diperoleh dari Globalforestwatch.com. Proses mengurangi variabel dari data yang diperoleh menghasil dampak yang sangat signifikan pada model klasifikasi. Terapat enam dan atau tujuh variabel yang sangat berpengaruh dalam menentukan titik panas, variabel tersebut jugalah yang menghasilkan model klasfikasi terbaik. XGBoost dan feature importance menghasilkan akurasi sebesar 89.52%. Sensitivity (SE), Specificity (SP), dan Matthews Correlation Coefficient (MCC).secara berturut turut 91.32 %, 93.16 % dan 92.75 %. Metode ini juga lebih baik dibandingkan dengan hasil penelitian sebelumnya.

Prediction Calculation of PT. Indofood Sukses Makmur Tbk. Stock using R Studio with Autoregressive Integrated Moving Average (ARIMA) Method Jonassen Kenrick; Yanti Yanti
Journal of Software Engineering, Information and Communication Technology (SEICT) Vol 2, No 2: December 2021
Publisher : Universitas Pendidikan Indonesia (UPI)

PT. Indofood Sukses Makmur Tbk is one of the consumer stocks with a parent company, namely PT. Indofood Sukses Makmur Tbk (INDF) is also in the consumer sector. In 2020, the impact of the coronavirus pandemic will be felt by the public and the government, one of which also has a significant effect on the economic sector. Macro companies show stock prices dropping drastically in early 2020 due to the pandemic. And that's where investors are tempted to buy shares. However, until now, the price of macro companies' claims, including INDF's shares, still fluctuates. So it is difficult to determine the future stock price. Therefore, research is needed to predict INDF stock prices in the future. This study aims to provide information about INDF stock prices in the future based on prediction results which investors can then use to read INDF stock charts in the future so that they do not experience capital loss. This research uses R Studio with Autoregressive Integrated Moving Average (ARIMA) method. Based on the research method carried out in input and data processing, checking stationarity, model specifications, parameter estimation, residual analysis, and forecasting, the results obtained regarding the prediction of INDF stock prices show fairly accurate results. This can be seen from the results of stock price predictions in February – April 2021 with the actual data available. Figures from the actual data are still included in the upper and lower limits of the predicted results.

Implementation of the K-Neighbors Algorithm to Detect Diabetes Web Based Application Mohammad Farrel Nur Rilwanu; Faris Huwaidi; Hibar Taufikurachman
Journal of Software Engineering, Information and Communication Technology (SEICT) Vol 3, No 1: June 2022
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/seict.v3i1.42347

Indonesia is the fifth country with the most diabetes sufferers in the world. This is influenced by an unhealthy lifestyle and then coupled with a lack of public awareness to check whether he has diabetes or not. The KNN (K-Nearest Neighbors) algorithm can be used to pr edict whether a person has diabetes. By using a dataset from the Pima Indian Diabetes Database, the data training process was carried out using the KNN algorithm and obtained decent accuracy results using a Jupyter notebook. From the results of the trained data set, it is then exported to be used in website development using the Python programming language. In the web application developed, the user is asked to input data on pregnancies (a person's pregnancy rate as long as he is alive), insulin levels, glucose levels, BMI, blood pressure, family history of diabetes, skin thickness, and age in the form of a slider. The input data is processed by the KNN algorithm to determine the outcome in the form of a positive or negative diabetes result based on the proximity of the new data entered with other data that has been trained.

Exploration of Spontaneous Speech Corpus Development in Urban Agriculture Instructional Videos Trisna Gelar; Aprianti Nanda
Journal of Software Engineering, Information and Communication Technology (SEICT) Vol 3, No 1: June 2022
Publisher : Universitas Pendidikan Indonesia (UPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/seict.v3i1.44548

Video transcription can be obtained automatically based on the original language translation of the video maker's speech, but the quality of the transcription depends on the quality of the audio signal and the natural voice of the speaker. In this study, Deep Speech is used to predict letters based on acoustic recognition without understanding language rules. The Common Voice multilingual corpus helps Deep Seech to transcribe Indonesian. However, this corpus does not accommodate the special topic of urban agriculture, so an additional corpus is needed to build acoustic and language models with the urban agriculture domain. A total of 15 popular videos with closed captions and nine E-Books with the theme of Horticulture (fruit, vegetables and medicinal plants) were curated. The video data were extracted into audio and transcription according to specifications as training data, while the agricultural text data were transformed into language models, which were used to predict recognition results. The evaluation results show that the number of epochs has an effect on improving the transcription performance. The language model score used during prediction improved WER performance as it interpreted words with agricultural terms. Another finding was that the model was unable to predict short words with informal varieties and located at the end of the sentence.

Page 2 of 4 | Total Record : 38

1 2 3 4

Filter by Year

2020 2023

Filter By Issues

All Issue Vol 4, No 1: June 2023 Vol 3, No 2: December 2022 Vol 3, No 2 (Desember 2022) Vol 3, No 1: June 2022 Vol 2, No 2: December 2021 Vol 2, No 1: June 2021 Vol 1, No 1: December 2020

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name
Dian Anggraini

Contact Email
dian.anggraini@upi.edu

Phone
+6285316735767

Journal Mail Official
seict@upi.edu

Editorial Address
Jl. Raya Cibiru KM 15, Cibiru Wetan, Bandung, Jawa Barat 40625

Location
Kota bandung,

Jawa barat

INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Dian Anggraini

Contact Email dian.anggraini@upi.edu

Phone +6285316735767

Journal Mail Official seict@upi.edu

Editorial Address Jl. Raya Cibiru KM 15, Cibiru Wetan, Bandung, Jawa Barat 40625

Location Kota bandung, Jawa barat INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Dian Anggraini

Contact Email
dian.anggraini@upi.edu

Phone
+6285316735767

Journal Mail Official
seict@upi.edu

Editorial Address
Jl. Raya Cibiru KM 15, Cibiru Wetan, Bandung, Jawa Barat 40625

Location
Kota bandung,

Jawa barat

INDONESIA