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All Journal International Journal of Informatics and Communication Technology (IJ-ICT) Jurnal Informatika dan Teknik Elektro Terapan Jurnal Eksplora Informatika JURNAL PENDIDIKAN TAMBUSAI JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jusikom: Jurnal Sistem Informasi Ilmu Komputer bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Jifosi PLAKAT : Jurnal Pelayanan Kepada Masyarakat Nusantara Science and Technology Proceedings Jurnal Teknik Informatika (JUTIF) Jurnal Ilmiah Teknologi Informasi dan Robotika JUSTIN (Jurnal Sistem dan Teknologi Informasi) International Journal Of Computer, Network Security and Information System (IJCONSIST) AJARCDE (Asian Journal of Applied Research for Community Development and Empowerment) Jurnal Ilmiah Teknik Informatika (TEKINFO) Sisfo: Jurnal Ilmiah Sistem Informasi Digital Transformation Technology (Digitech) MASALIQ: Jurnal Pendidikan dan Sains JEECS (Journal of Electrical Engineering and Computer Sciences) Saber: Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Journal of Innovative and Creativity Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) International Journal of Information Engineering and Science Jurnal Inovasi Artificial Intelligence & Komputasional Nusantara
Ani Dijah Rahajoe
Department Of Computer Science, Faculty Of Informatics, Universitas Pembangunan Nasional “Veteran” Jawa Timur, Surabaya 60294, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Transportation Other

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Implementation of Ad-Hoc Protocol On Tandem Multihop Wireless Network Agussalim Agussalim; Dhian Satria Yudha Kartika; Ani Dijah Rahajoe
Jurnal Ecotipe (Electronic, Control, Telecommunication, Information, and Power Engineering) Vol 9 No 2 (2022): Jurnal Ecotipe, October 2022
Publisher : Jurusan Teknik Elektro, Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/jurnalecotipe.v9i2.3305

Abstract

The utilization of Internet of Things (IoT) technology, especially in remote areas, is still relatively low, even though the technology is required to implement smart farming or smart villages, which aims to improve the quality of life of people in rural areas. The high investment cost for IoT networks that still use cellular networks or Wi-Fi is one of the causes of the slow implementation of this technology. Our previous research has developed an alternative network for IoT devices in remote areas with the concept of a Tandem Multihop Wireless Network focusing on developing simple message scheduling. This research focuses on implementing ad-hoc routing protocols in tandem with multi-hop wireless to analyze the advantages and disadvantages of the protocol. Each sensor periodically sends data to the monitoring server via IoT devices on each tower. The scenario was implemented using MININET-WIFI. Evaluations were carried out to determine delivery probability, latency average, and jitter. In general, the two Ad-Hoc protocols tested, namely OLSR and BATMAN, had the same performance when the data sent was 1 MB, but when the data size was increased to 2 MB, the OLSR routing protocol on several nodes had better performance than BATMAN.
Prediksi Jumlah Pengunjung Perperiode Terhadap Tempat Wisata Pantai Menggunakan Triple Exponential Smoothing (Studi Kasus Pantai Gili Labak Sumenep) Ainur Rahim; Ani Dijah Rahajoe; M. Mahaputra
Jurnal Ilmiah Teknologi Informasi dan Robotika Vol. 3 No. 2 (2021): Jurnal Ilmiah Teknologi Informasi dan Robotika
Publisher : Universitas Pembangunan Nasional Veteran Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/jifti.v3i2.66

Abstract

Pariwisata menjadi salah satu sektor dalam peningkatan pendapatan suatu wilayah, baik negara, daerah ataupun kabupaten. Begitu halnya di kabupaten sumenep wisata terdapat wisata religi, kuliner, keraton dan bahari. Keberadaan wisata bahari (pantai) menjadi fokus pnelitian penulis. Bahwa Sumenep atau lebih tepatnya Gili Labak dengan wisata pantainya menjadi tempat kunjungan dominan oleh wisatawan khusunya dikalangan remaja. Penelitian ini bertujuan untuk membangun aplikasi Prediksi Jumlah Pengunjung Perperiode Terhadap Tempat Wisata Pantai Menggunakan Triple Exponential Smoothing (Studi Kasus Pantai Gili Labak Sumenep). Data wisata sebelumnya merupakan data pada tahun 2015-2018 dan hasil prediksi periode 2019 diperoleh sebesar 32.369. Metode Triple Exponential Smoothing Holt –Winter Model Multiplikatif menggunakan konstanta hasil kesalahan yang paling kecil yaitu nilai konstanta alfa (α) = 0,1, beta (β) = 0,8 dan gamma (ƴ) = 0,1. Kesalahan (error) yaitu MAD sebesar 0.053, MSE sebesar 0.003, MAPE sebesar 0.002 dan MPE -0.491.
Klasifikasi Wajah Kantuk Menggunakan Parameter Wajah Dengan Algoritma Long Short Term Memory Agung Subekti, Mohamad Rafli; Rahajoe, Ani Dijah; Mandyartha, Eka Prakarsa
JIFOSI Vol. 5 No. 2 (2024): Integrasi Sistem Cerdas dengan Internet of Things (IoT)
Publisher : UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/jifosi.v5i2.145

Abstract

Drowsiness is one of the main factors that influences performance and safety, especially in driving activities and productivity levels. This research develops a Drowsiness facial classification system using facial parameters such as Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR), as well as the Long Short-Term Memory (LSTM) algorithm. Data is collected via video of the subject's face and facial parameters are calculated from facial landmarks extracted using the dlib library. The LSTM model was chosen because of its ability to capture important temporal patterns in detecting changes in Drowsiness over time. With a data sequence of five frames as input, the dataset is divided into 80% training data and 20% test and validation data. Experimental results show that the LSTM model is able to detect drowsiness with high accuracy, showing that the combination of EAR and MAR is effective in identifying drowsiness. This system is expected to be applied in early warning systems for drivers and employee monitoring, making significant contributions in the field of drowsiness detection using LSTM algorithms and facial parameters.
PENGEMBANGAN APLIKASI PENDETEKSI KERETAKAN JALAN BERBASIS ANDROID DENGAN IMPLEMENTASI ALGORITMA HYBRID CNN-LSTM Pradana, Ilham Akbar; Ani Dijah, Rahajoe; Sihananto, Andreas Nugroho
JIFOSI Vol. 5 No. 2 (2024): Integrasi Sistem Cerdas dengan Internet of Things (IoT)
Publisher : UPN "Veteran" Jawa Timur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33005/jifosi.v5i2.146

Abstract

Infrastruktur jalan yang berkualitas memegang peran penting dalam pertumbuhan ekonomi suatu negara. Namun, dengan meningkatnya volume kendaraan dan faktor lingkungan, kerusakan jalan menjadi masalah yang tak terhindarkan dan memerlukan perhatian serius. Metode tradisional dalam mendeteksi kerusakan jalan seringkali dilakukan melalui inspeksi manual yang tidak hanya memakan waktu tetapi juga cenderung subjektif dan kurang akurat. Penelitian ini mengusulkan pengembangan aplikasi Android yang inovatif, yang memanfaatkan teknologi Deep Learning untuk mendeteksi kerusakan jalan secara akurat dan efisien. Aplikasi ini menggabungkan Convolutional Neural Network (CNN) untuk ekstraksi ciri visual dari gambar dan Long Short-Term Memory (LSTM) untuk memahami konteks sekuensial dari data hasil luaran lapisan-lapisan CNN. Dataset yang digunakan dalam pengembangan model ini bersumber dari kumpulan gambar kerusakan jalan sebagai representasi dari berbagai kondisi jalanan perkotaan di Indonesia. Melalui proses pelatihan, model CNN-LSTM yang ini diintegrasikan ke dalam aplikasi dengan menggunakan TensorFlow Lite. Pengembangan aplikasi Android dilakukan dengan mempertimbangkan arsitektur aplikasi yang baik dan efisien, menjamin bahwa aplikasi tidak hanya responsif dan intuitif tetapi juga hemat sumber daya. Melalui integrasi teknologi canggih dan pendekatan pengembangan yang terfokus, aplikasi ini berpotensi menjadi alat penting dalam usaha pemeliharaan infrastruktur jalan, memberikan solusi yang praktis dan inovatif untuk mendeteksi kerusakan jalan dengan cepat dan akurat.
Analisis Sentimen Kendaraan Listrik Pada Twitter Menggunakan Metode Long Short Term Memory Dian Agus Prawinata; Ani Dijah Rahajoe; I Gede Susrama Mas Diyasa
SABER : Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi Vol. 2 No. 1 (2024): Januari : Jurnal Teknik Informatika, Sains dan Ilmu Komunikasi
Publisher : STIKes Ibnu Sina Ajibarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59841/saber.v2i1.857

Abstract

In facing the increasing awareness of environmental impact, electric vehicles have become a primary focus in the global automotive industry. With the advancement of technology and the growing need for eco-friendly solutions, the evaluation of public sentiment towards electric vehicles becomes highly relevant. This research aims to analyze opinions expressed on Twitter regarding the use of electric vehicles using the Long Short Term Memory (LSTM) classification method. Utilizing a dataset of 30,000 entries, this study applies the LSTM algorithm to classify sentiment in tweets. Four different scenarios are tested, involving combinations of Continuous Bag of Words (CBOW) and Skip-Gram feature extraction methods, as well as data split percentages of 80:20 and 70:30. The research results demonstrate high accuracy levels across all scenarios, ranging from 85.16% to 85.9%. These findings indicate the effectiveness of sentiment analysis in gauging public perspectives on the use of electric vehicles. This study makes a significant contribution to understanding public sentiment related to electric vehicles based on Twitter data while highlighting the application of sentiment analysis techniques in the context of electric vehicle usage.
Utilization Of Discord Bots In Providing Manhwa Recommendations Using Content-Based Filtering Method Muhammad Farhan Maulana; Ani Dijah Rahajoe; Made Hanindia Prami Swari
Jurnal Sistem Informasi dan Ilmu Komputer Vol. 8 No. 1 (2024): JUSIKOM: JURNAL SISTEM INFROMASI ILMU KOMPUTER
Publisher : Fakultas Teknologi dan Ilmu Komputer Universitas Prima Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34012/jurnalsisteminformasidanilmukomputer.v8i1.5600

Abstract

The high number of manhwa released today makes it difficult for readers to find manhwa that match their preferences, especially when trying to find manhwa that are similar to ones they have read before. The manual search process, either through recommendations from communities or online forums, often results in subjective and inconsistent suggestions. To address this issue, a Discord bot was developed that utilizes the Content-Based Filtering method as an automated solution to manhwa recommendation. This method uses the Cosine Similarity algorithm to measure the similarity between manhwa based on features such as title, genre, synopsis, and author. For comparison, the Euclidean Distance algorithm is used to evaluate the accuracy and performance of the recommendation. From the test results, the Cosine Similarity algorithm showed superior performance in providing recommendations based on the questionnaire results and showed a high level of user satisfaction with the developed Discord bot.
PENERAPAN DATA MINING PADA TRANSAKSI PENJUALAN SELAMA BULAN RAMADHAN UNTUK MENENTUKAN MARKET BASKET ANALYSIS MENGGUNAKAN ALGORITMA ECLAT Putra Bramantyo, Adam; Ani Dijah Rahajoe, Rr; Kartini, Kartini
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 8 No. 3 (2024): JATI Vol. 8 No. 3
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v8i3.9838

Abstract

Semakin banyak dan menjamurnya berbagai toko kebutuhan seahri-hari di Indonesia membuat persaingan bisnis dibidang ini juga semakin ketat. Hampir di setiap daerah terdapat toko kebutuhan sehari hari, mulai dari toko kelontong, warung madura, minimarket, hingga supermarket. Banyak dan ketatnya persaingan dalam bisnis usaha ini mendorong perlunya inovasi dan strategi baru agar dapat tetap bertahan dan tumbuh menjadi lebih berkembang. Meningkatkan penjualan di berbagai event besar tahunan bisa menjadi opsi untuk mengembangkan usaha umkm ini, salah satunya adalah pada bulan Ramadhan. Pada kondisi tersebut dibutuhkan teknik yang dapat mengolah data menjadi informasi yang bermanfaat. Salah satu contoh teknik yang dapat digunakan adalah data mining yang dapat berfungsi untuk mengetahui produk-produk apa saja yang sering dibeli secara bersamaan. Metode analisis yang digunakan adalah market basket analysis menggunakan algoritma Eclat, dimana algoritma ini berfungsi untuk menentukan himpunan data yang paling sering muncul (frequent itemset). Hasil dari penelitian didapatkan bahwa terdapat 6 frequent itemset yang memenuhi nilai minimum support dan confidence, dengan nilai support dan confidence tertinggi ialah Sariwangi30 – EnervonCMtv4 dengan nilai Support 0.82% dan Confidence 48% sebagai strong rule. Hal ini dapat menjadi pengetahuan baru dan evaluasi produk, yang diharapkan dapat meningkatkan penjualan pada Bulan Ramadhan dan agar dapat berkembang lebih pesat
Implementation of the Quizizz Application for Learning Media in Nurul Islam Kindergarten Rungkut Asri Timur Surabaya Based on Gamification Ani Dijah Rahajoe; Agussalim; Hendra Maulana
Nusantara Science and Technology Proceedings 7st International Seminar of Research Month 2022
Publisher : Future Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/nstp.2023.3376

Abstract

The Covid-19 pandemic forces us to continue to innovate and adapt to continue to carry out activities as before the pandemic. This digital adaptation or transformation needs to be done to minimize direct human contact. The educational institution, which is the meeting place for students, and teachers, makes the location quite vulnerable to the spread of Covid19. Educational institutions and university organizers are also encouraged to conduct online learning. Online learning media that can use include Quizizz and Kahoot. Quizizz and Kahoot are used to design quizzes, tests, polls, surveys, or diagnostic tests that can be separated from the primary teaching and learning process or are supplementary. This learning is applied to the teachers of TK Nurul Islam Rungkut Asri Timur Surabaya. During the two days of training, the teachers showed enthusiasm during the training. The methods applied are discussion, demonstration, and hands-on practice. Six kindergarten teachers and two lecturers from UPN “Veteran” Jawa Timur attended this training. The training results show that the method can align the learning media through Quizziz Kahoot with the gamification method for students. The results are indicated by the material presented, including pictures adapted to the student's age. The training results also show that participants can directly practice learning materials for their students, and their students can participate more actively in the gamification process. The feedback showed that all teacher participants stated that learning in Nurul Islam Kindergarten was appropriate, and 83% stated it was easy to implement.
IMPLEMENTASI METODE COLLABORATIVE FILTERING MENGGUNAKAN ALGORITMA COSINE SIMILARITY DAN JACCARD SIMILARITY PADA SISTEM E-COMMERCE Waskito, Muhammad Rizal; Rahajoe, Ani Dijah; Nurlaili, Afina Lina
Jurnal Informatika dan Teknik Elektro Terapan Vol 12, No 3S1 (2024)
Publisher : Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/jitet.v12i3S1.5315

Abstract

Peningkatan pesat e-commerce telah mengubah pola belanja konsumen, menciptakan kebutuhan akan sistem rekomendasi produk yang lebih personal dan relevan. Tantangan yang sering muncul adalah ketidakmampuan sistem rekomendasi dalam menangani data sparsity dan memberikan rekomendasi yang akurat, terutama ketika data pengguna terbatas. Untuk mengatasi tantangan ini, penelitian ini mengimplementasikan metode Collaborative Filtering dengan algoritma Cosine Similarity dan Jaccard Similarity. Cosine Similarity digunakan untuk mengukur kemiripan antar pengguna berdasarkan nilai yang diberikan terhadap produk, sementara Jaccard Similarity fokus pada kesamaan interaksi pengguna tanpa memperhatikan nilai rating. Hasil penelitian menunjukkan bahwa Cosine Similarity cenderung memberikan skor kemiripan yang lebih tinggi dibandingkan Jaccard Similarity, terutama dalam situasi data yang tidak lengkap, dengan rata rata selisih skor sebesar 26.14%. Selain itu, sistem yang dikembangkan mampu memanfaatkan efek Fear of Missing Out (FoMO) untuk meningkatkan relevansi dan urgensi pembelian produk. Integrasi algoritma ini meningkatkan akurasi rekomendasi dan membuka peluang untuk pengembangan lebih lanjut, seperti penerapan hybrid filtering, guna mengoptimalkan kinerja sistem rekomendasi dalam e-commerce.
Hybrid Neural Network-Based Road Damage Detection Using CNN-RNN and CNN-MLP Models Rahajoe, Ani Dijah; Suriansyah, Muhammad; Jr, Angelo A. Beltran
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4435

Abstract

Currently, there are many applications of image processing in various fields. One of them is the recognition of paved road images. Detection through images helps in handling infrastructure development roads. With the advancement of technology, especially in the field of deep learning, the process of detecting road damage can be done automatically and more efficiently. The road damage detection system can be integrated into the smart city system to monitor infrastructure conditions in real time. This study will use a combined deep learning algorithm between Convolutional Neural Network- Recurrent Neural Network (CNN-RNN) and as a comparison using Convolutional Neural Network- MultiLayer Perceptrons (CNN-MLP). The study aims to analyze the accuracy of using the CNN-RNN and CNN-MLP algorithms for detecting paved roads that have categories of undamaged roads, damaged roads, and damaged roads with holes. The detection of paved roads has complex details so an algorithm that has good performance with high accuracy is needed. The results of the study showed that the CNN-RNN hybrid had a better accuracy of 96.59 percent than the CNN-MLP hybrid model of 95.9 percent.  
Co-Authors Achmad Ario Dwi Maulana Agung Subekti, Mohamad Rafli Agussalim Agussalim Agussalim, Agussalim Ainur Rahim Akash, Fransisco Rivaldi Andre Leto Andreas Nugroho Sihananto Andriano Lukas Angelo A Beltran Angelo A Beltran Jr Angga Dwi Cahyono Aninditya Daniar Anna Fauziah arif arizal Arif Arizal Arif Dwi Putra Arif Setyo Wibowo Aryo Bagus Satrio Wicaksono Azaidane, Dandi Azmi Maulana Mahardika Bawazir Fadhil Mohammad Bawazir Fadhil Muhammad Bimantoro, Bisma Satrio Brahmantio Widyo Trenggono Budi Mukhamad Mulyo Chaurina, Agfanadita Rezkia Denisa Septalian Alhamda Dhian Satria Yudha Kartika Dian Agus Prawinata Eka Prakarsa Mandyartha Eva Yulia Puspaningrum Fairus Irhab Adinata Efendi Fajar Indra Nur Alam Feriza, Reyana Dinda Maulan Fransiska, Amelia Hamdan Yuwafi Mastu Wijaya Henni Endah Wahanani I Gede Susrama Mas Diyasa I Gede Susrama Mas Diyasa Ida Retno Moeljani Indartono, Taqiyya Irsyad Rafi Naufaldi Jr, Angelo A. Beltran Kartini Kartini Khansa Alyssa Fauziyah M Mahaputra M. Mahaputra Made Hanindia Prami Swari Mas Nurul Hamidah Maulana, Hendra Megantara, Sofia Ramadhani Muchamad Dicky Alifiansyah Muchlisiniyati Safeyah Muhammad Aldi Maulana Muhammad Fahreal Bernov Muhammad Farhan Maulana Muhammad Iqbal Al Afgany Muhammad Rizky Firdaus Muhammad Suriansyah Muttaqin, Faisal Nadia Dita Salsabila Nafiendra Praba Hendyka Nurlaili, Afina Lina Nurlaili, Afina Lina P. Eko Prasetyo Pahlevy, Mohammad Reza Pangesti N Perkasa, Laurensius Gading Surya Piter Rudi Irson Rivaldo Lapon Pradana, Ilham Akbar Pramnesti, Adisty Regina Putra Bramantyo, Adam Putra, Brian Akhdan Rangga Laksana Aryananda Retno Mumpuni Retno Mumpuni Reza Aminullah Rifki Fahrial Zainal Rino Zakaria Satrio Budi Wahyuono Shagi Hisyam Al Fathony Soffiana Agustin, Soffiana Subekti, Mohamad Rafli Agung Suriansyah, Muhammad Suryantari, Putu Anggi Syariful Alim Waskito, Muhammad Rizal Winarko, Edi Yushinta Aristina Sanjaya