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All Journal Jurnal Sistem Komputer BERKALA SAINSTEK Jurnal Simetris INFORMAL: Informatics Journal JURNAL NASIONAL TEKNIK ELEKTRO Proceeding of the Electrical Engineering Computer Science and Informatics Jurnal Komputer Terapan Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control IJAIT (International Journal of Applied Information Technology) Jurnal Riset Rekayasa Elektro Sibatik Journal : Jurnal Ilmiah Bidang Sosial, Ekonomi, Budaya, Teknologi, Dan Pendidikan Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) Jurnal Pengabdian Masyarakat
Oktavia, Nelly
Universitas Jember
Agriculture, Biological Sciences & Forestry Arts Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Mathematics Physics Social Sciences

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Implementasi Metode Holt-Winters Multiplicative pada Sistem Peramalan Pengunjung Objek Wisata Kawah Ijen Kabupaten Bondowoso Irawan, Hendry Sakti; Adiwijaya, Nelly Oktavia; Furqon, Muhammad ‘Ariful
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 14, No 2 (2023): JURNAL SIMETRIS VOLUME 14 NO 2 TAHUN 2023
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v14i2.9549

Abstract

Kawah Ijen merupakan salah satu destinasi wisata dengan tingkat kepadatan pengunjung paling tinggi diantara seluruh objek wisata di Kabupaten Bondowoso. Kendala yang paling sering dialami oleh wisata Kawah Ijen pada tahun 2017 - 2022 yaitu terkait jumlah kedatangan pengunjung yang tidak menentu dan peningkatan maupun penurunan yang signifikan di bulan tertentu. Pola seperti ini dinamakan pola data musiman dan tren. Metode Holt-Winters Multiplicative ini dianggap sangat tepat digunakan untuk peramalan dengan pola data musiman dan tren. Pengukuran tingkat kesalahan yang digunakan pada penelitian ini menggunakan metode MAPE. Hasil dari perhitungan nilai MAPE tanpa melampirkan data 2020 menghasilkan nilai MAPE yaitu sebesar 9 %, sedangkan hasil dari perhitungan MAPE dengan melampirkan data 2020 menghasilkan nilai MAPE yaitu sebesar 209 %. Hal ini menunjukkan bahwa terdapat dua perbandingan perhitungan MAPE dengan metode Holt-Winters Multiplicative. Dapat disimpulkan bahwa perhitungan metode HoltWinters Multiplicative tanpa melampirkan data 2020 memiliki MAPE dengan nilai 9 % dapat dikatakan sangat rendah karena memiliki rata-rata nilai MAPE dibawah 10%.
Comparison Analysis of Dijkstra and A-Star Algorithms in NPC (Non-Playable Character) Movement on a Single-Player Game: Case Study: Chaos Crossing Game Dhaifullah, Dany Zaky; Adiwijaya, Nelly Oktavia; Pandunata, Priza
IJAIT (International Journal of Applied Information Technology) Vol 08 No 01 (May 2024)
Publisher : School of Applied Science, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25124/ijait.v8i1.6053

Abstract

Artificial intelligence in a game plays a vital role in enhancing the player's gaming experience, especially in single-player games. NPCs are the primary means of interaction in single-player games, assisting and guiding players like interactions with other players. Chaos Crossing requires pathfinding technology for optimal NPC movement, allowing them to navigate the environment grid-based while avoiding static obstacles. The Dijkstra algorithm and the A-Star algorithm need to be compared because, based on previous research, the Dijkstra algorithm has proven effective for calculating the shortest distance to the destination point in a static environment based on a two-dimensional grid with characters moving in it, as well as the A-Star algorithm can avoid a static environment based on grid and is used to determine the shortest distance to the destination point in the character's movement. This quantitative research aims to find a solution that optimizes NPC movement by testing and comparing Dijkstra's and A-Star's algorithms in a static environment grid based on the game Chaos Crossing. The test results and comparative analysis show that the A-Star algorithm performs a faster route search with an average value of 36.37 seconds than Dijkstra's algorithm with an average matter of 20.76 seconds and utilizes memory more efficiently with an average value of 20.19 MB than Dijkstra's algorithm with a value 22.17 MB on average. However, Dijkstra's algorithm produces a slightly shorter track distance, with an average value of 42.26 units, compared to the A-Star algorithm, with an average value of 42.39 units.
Penguatan Pengelola Lahan Kelengkeng di Perkebunan Sentool melalui Teknologi Berbasis IoT Maududie, Achmad; Swasono, Dwiretno Istiyadi; Adiwijaya, Nelly Oktavia
JURNAL PENGABDIAN MASYARAKAT (JPM) Vol 4 No 2 (2024)
Publisher : Institut Teknologi dan Sains Mandala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31967/jpm.v4i2.1231

Abstract

Sentool Plantation is one of the plantations in Jember Regency that is currently promoting longan as one of its superior products. From the results of previous observations, two problems were found, namely workers having difficulty in monitoring the land and inefficient use of water resources. To overcome these problems, an appropriate technology for controlling irrigation and monitoring land conditions based on the Internet of Things (IoT) is prepared that can help Sentool plantation workers in managing the longan plantation area. The implementation of this activity is divided into three stages, namely: development of irrigation control system and monitoring of land conditions, system integration, and implementation and socialization to plantation employees as the use of the system. This activity has succeeded in realizing the intended system in the form of hardware and software to control the irrigation system and monitoring with four required indicators, namely soil moisture, air humidity, air temperature, wind speed, and rainfall measurements. At the socialization stage, the enthusiasm of the Sentool plantation employees can be seen from the liveliness in participating in the socialization stage of the use of the system to assist land management. Currently, the plantation employees also know how to operate the system so that they can reduce the constraints of the irrigation system and can see the condition of the longan fields through the application.
Sentiment Analysis of Skincare Active Ingredient Topics using Latent Dirichlet Allocation and InSet Lexicon on Twitter Social Media Nuarie, Aurila; Adiwijaya, Nelly Oktavia; Dharmawan, Tio
INFORMAL: Informatics Journal Vol 9 No 3 (2024): Informatics Journal (INFORMAL)
Publisher : Faculty of Computer Science, University of Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.19184/isj.v9i3.46116

Abstract

The cosmetic industry, encompassing skincare, underwent a growth rate of up to 9.61%, as indicated by data from the Central Statistics Agency (BPS). With the ongoing expansion of the cosmetic sector, the production of products, particularly those featuring active ingredients in skincare, increased accordingly. Consequently, the utilization of these active ingredients witnessed an upward trend. Twitter data pertaining to active skincare ingredients was collected, forming a substantial dataset that required methods for analyzing topics and opinions.To identify latent topic information, topic modeling using Latent Dirichlet Allocation (LDA) was employed. Prior to conducting topic modeling, clustering was initially performed using K-Means to facilitate the categorization of the extensive dataset into more specific data groups. Subsequently, sentiment analysis was carried out using the InSet Lexicon. The research resulted in four clusters, each of which underwent topic modeling with LDA.Cluster 1 unveiled a topic focusing on the content of alpha arbutin, with sentiment results of 42.5% positive, 45% negative, and 12.5% neutral. Cluster 2 centered around the content of reinol and AHA BHA, with sentiment results of 41.36% positive, 46.99% negative, and 12.13% neutral. Cluster 3 delved into the content of salicylic acid and hyaluronic acid, with sentiment results of 40.57% positive, 42.62% negative, and 16.80% neutral. Lastly, Cluster 4 discussed the clay mask "Skintific" containing mugwort, with sentiment results of 41.67% positive, 43.94% negative, and 14.39% neutral.This research is anticipated to be beneficial and can be utilized by the skincare industry to update the company's business strategies.
Detecting Acute Lymphoblastic Leukemia in Blood Smear Images using CNN and SVM Adiwijaya, Nelly Oktavia; Ardiansyah, Sultan; Swasono, Dwiretno Istiyadi
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 1, February 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i1.2027

Abstract

Acute Lymphoblastic Leukemia (ALL) is a common and aggressive subtype of leukemia that predominantly affects children. Accurate and timely diagnosis of ALL is critical for successful treatment, but it is hindered by the limitations of manual examination of peripheral blood smear images, which are prone to human error and inefficiency. This study proposes an improved diagnostic approach by integrating the EfficientNet architecture with a Support Vector Machine (SVM) classifier to enhance classification accuracy and address the performance inconsistencies of standalone EfficientNet models. Additionally, a novel CNN-based model with a reduced number of parameters is developed and evaluated. A dataset comprising 3.256 peripheral blood smear images across four classes (benign, early, pre and pro) was used for training and testing. The EfficientNet-SVM models achieved a peak accuracy of 97.35% using the EfficientNet-B3 architecture, surpassing previous studies. The improved CNN model achieved the highest accuracy of 99.18% while reducing parameters by 59.5% compared to the best prior models, with a negligible accuracy decrease of only 0.67%. These findings highlight the potential of combining EfficientNet with SVM and the efficiency of the improved CNN model for automated ALL detection, paving the way for more reliable, cost-effective, and scalable diagnostic tools.
Optimasi Model Rekomendasi Topik Skripsi berdasarkan Performa Akademik Mahasiswa menggunakan SMOTE Adiwijaya, Nelly Oktavia; Al Abror, Muhammad Farhan; Dharmawan, Tio; Hidayat, Muhamad Arief
Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) 2025: SNESTIK V
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/p.snestik.2025.7055

Abstract

Sekitar 68% mahasiswa mengalami keterlambatan dalam menyelesaikan skripsi yang mengindikasikan adanya kesulitan dalam penentuan topik penelitian sesuai dengan minat dan keahlian.Ketidaksesuaian ini seringkali disebabkan kurangnya pemahaman mahasiswa terhadap kemampuan akademik yang dimiliki. Hal ini berdampak signifikan pada keterlambatan kelulusan mahasiswa.Penelitian ini bertujuan mengatasi permasalahan tersebut dengan membangun model klasifikasi untuk membantu mahasiswa dalam menentukan topik skripsi berdasarkan kemampuan akademik mereka. Indikator yang digunakan berupa transkrip nilai mata kuliah mahasiswa dari semester 1 hingga semester 6. Penelitian ini menggunakan metode Feature Selection dan SMOTE sebelum dilakukan pemodelan untuk meningkatkan kualitas data. Dua algoritma Support Vector Machine (SVM) dengan kernel RBF dan Naive Bayes tipe kategorikal digunakan untuk membangun model klasifikasi. Berdasarkan hasil analisis yang diperoleh bahwa penerapan SMOTE untuk penanganan data sebelum diklasifikasi berpengaruh sangat baik terhadap hasil akurasi. Algoritma Support Vector Machine dengan kernel RBF memberikan akurasi tertinggi sebesar 96.81% sedangkan Naive Bayes tipe Categorical menghasilkan akurasi 83.75%. Hasil penelitian ini memberikan solusi praktis bagi mahasiswa dalam memilih topik skripsi yang relevan dengan kemampuan mereka dimana mata kuliah yang terkait dengan setiap topik skripsi dapat berbeda-beda untuk masing-masing mahasiswa.
Co-Authors Achmad Maududie Agus Dwi Milniadi Agustinus Mariano Galwargan Ahmad Sonny Pranata Al Abror, Muhammad Farhan Antonius Cahya Prihandoko Ardiansyah, Sultan Dhaifullah, Dany Zaky Dharmawan, Tio Dwiretno Istiyadi Swasono Fathur Rozi, Muhammad Iqbal Furqon, Muhammad Ariful Galwargan, Agustinus Mariano Haris Haris Hidayat, Muhamad Arief Irawan, Hendry Sakti M, Nurul Istiana Nuarie, Aurila Nurul Istiana M Priza Pandunata Putra, Januar Adi Qurrota A'yuni Ar Ruhimat Ramadhani, Suci Nur S Slamin Saiful Bukhori Wijaya, Angga Ari