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All Journal Register: Jurnal Ilmiah Teknologi Sistem Informasi Buana Matematika : Jurnal Ilmiah Matematika dan Pendidikan Matematika Indonesian Journal of Combinatorics
Nisa Ayunda
Universitas Pesantren Tinggi Darul Ulum
Computer Science & IT Decision Sciences, Operations Research & Management Education Mathematics

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Another H-super magic decompositions of the lexicographic product of graphs H Hendy; Kiki A. Sugeng; A.N.M Salman; Nisa Ayunda
Indonesian Journal of Combinatorics Vol 2, No 2 (2018)
Publisher : Indonesian Combinatorial Society (InaCombS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (915.399 KB) | DOI: 10.19184/ijc.2018.2.2.2

Abstract

Let H and G be two simple graphs. The concept of an H-magic decomposition of G arises from the combination between graph decomposition and graph labeling. A decomposition of a graph G into isomorphic copies of a graph H is H-magic if there is a bijection f : V(G) ∪ E(G) → {1, 2, ..., ∣V(G) ∪ E(G)∣} such that the sum of labels of edges and vertices of each copy of H in the decomposition is constant. A lexicographic product of two graphs G1 and G2,  denoted by G1[G2],  is a graph which arises from G1 by replacing each vertex of G1 by a copy of the G2 and each edge of G1 by all edges of the complete bipartite graph Kn, n where n is the order of G2. In this paper we provide a sufficient condition for $\overline{C_{n}}[\overline{K_{m}}]$ in order to have a $P_{t}[\overline{K_{m}}]$-magic decompositions, where n > 3, m > 1,  and t = 3, 4, n − 2.
Analisa Peramalan Data Time-Series Dengan Aplikasi Windows POM-QM Nisa Ayunda; Faizah; Sujarwo
Buana Matematika : Jurnal Ilmiah Matematika dan Pendidikan Matematika Vol 11 No 2 (2021)
Publisher : Universitas PGRI Adi Buana Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36456/buanamatematika.v11i2.5913

Abstract

Peramalan data time-series kini menjadi suatu kebutuhan dalam dunia bisnis maupun lingkungan hidup. Software Windows POM – QM memiliki tools dan beberapa metode yang dapat digunakan untuk peramalan data time-series. Diperlukan sebuah analisa hasil peramalan untuk mengetahui efektivitas dan akurasi metode-metode tersebut dalam meramalkan data time series agar dapat digunakan sebagai referensi metode dalam mendapatkan hasil yang optimal. Nilai MAPE digunakan sebagai ukuran perbandingan dalam analisa hasil perbandingan peramalan. Data time series yang digunakan adalah data curah hujan di Kabupaten Jombang. Hasil peramalan dengan metode Exponential Smoothing memiliki rata – rata nilai MAPE terkecil dibandingkan dengan metode lainnya yaitu sebesar 2,4 dengan pola data hasil peramalan berfluktuasi sebagaimana data sebenarnya. Sehingga penggunaan metode Exponential Smoothing dapat digunakan sebegai referensi dalam peramalan data time series dengan pola data musiman dengan menggunakan software Windows POM-QM.
Drowsy Eyes and Face Mask Detection for Car Drivers using the Embedded System Rizqi Putri Nourma Budiarti; Bagoes Wahyu Nugroho; Nisa Ayunda; Sritrusta Sukaridhoto
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 9 No 1 (2023): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v9i1.2612

Abstract

Efforts to prevent the spread of the COVID-19 virus have underscored the critical importance of mask-wearing as a preventive measure. Concurrently, road traffic accidents, often resulting from human error, have emerged as a significant contributor to global mortality rates. This study endeavors to address these pressing issues by employing advanced Deep Learning techniques to detect mask usage and identify drowsy eyes, thus contributing to the prevention of COVID-19 and accidents due to driver fatigue. To achieve this objective, an embedded system was developed, utilizing the integration of hardware and software components. The system effectively utilizes MobileNetV2 for face mask detection and employs HOG and SVM algorithms for drowsy eye detection. By seamlessly integrating these detection systems into a single embedded device, the simultaneous detection of both mask usage and drowsy eyes is made possible. The results demonstrates a commendable accuracy rate of 80% for face mask detection and 75% for drowsy eye detection. Furthermore, the mask detection component exhibits a remarkable training accuracy of 99%, while the drowsy eye detection component demonstrates an 80% training accuracy, affirming the system's efficacy in precisely identifying masks and drowsy eyes. The proposed embedded system offers potential applications in enhancing road safety. Its capability to effectively detect drowsy eyes and mask usage in car drivers contributes significantly to preventing accidents due to driver fatigue. Additionally, it plays a vital role in mitigating COVID-19 transmission by promoting widespread mask-wearing among individuals. This study exemplifies the potential of integrating Deep Learning methodologies with embedded systems, thus paving the way for future research and development in the realm of driver safety and virus prevention.
Co-Authors A.N.M Salman Bagoes Wahyu Nugroho Budiarti, Rizqi Putri Nourma Faizah H Hendy Kiki A. Sugeng Sujarwo