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All Journal Jurnal Statistika Universitas Muhammadiyah Semarang JMPM: Jurnal Matematika dan Pendidikan Matematika Desimal: Jurnal Matematika BAREKENG: Jurnal Ilmu Matematika dan Terapan Dinamisia: Jurnal Pengabdian Kepada Masyarakat JTAM (Jurnal Teori dan Aplikasi Matematika) Jurnal Penelitian dan Pengabdian Kepada Masyarakat UNSIQ Jambura Journal of Mathematics Jurnal Matematika UNAND Variance : Journal of Statistics and Its Applications ILKOMNIKA: Journal of Computer Science and Applied Informatics InPrime: Indonesian Journal Of Pure And Applied Mathematics Jurnal Statistika dan Aplikasinya Jambura Journal of Mathematics Education Indonesian Journal of Applied Research (IJAR) JAMBURA JOURNAL OF PROBABILITY AND STATISTICS Jurnal Diferensial Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi International Journal of Health, Economics, and Social Sciences (IJHESS) Griya Journal of Mathematics Education and Application Unnes Journal of Mathematics Journal of Fundamental Mathematics and Applications (JFMA) Research in the Mathematical and Natural Sciences Pattimura International Journal of Mathematics (PIJMath) Bulletin of Applied Mathematics and Mathematics Education Jurnal Riset Mahasiswa Matematika Jurnal Pengabdian Pada Masyarakat Indonesian Journal of Mathematics and Applications Hexagon: Jurnal Ilmu dan Pendidikan Matematika Journal of Mathematics, Computation and Statistics (JMATHCOS) System Information and Computer Technology (SYNCTECH) Indonesian Journal of Computational and Applied Mathematics
Salmun K. Nasib
Universitas Negeri Gorontalo
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Evaluation of the Adaptive Fuzzy Neuro Inference System and Fuzzy Model Time Series Markov Chains in Forecasting Crude Oil Prices Hinelo, Ikrar Prasetyo; Nuha, Agusyarif Rezka; Hasan, Isran K; Nasib, Salmun K; Abdussamad, Siti Nurmardia
Journal of Mathematics, Computations and Statistics Vol. 8 No. 1 (2025): Volume 08 Nomor 01 (April 2025)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/jmathcos.v8i1.6763

Abstract

The development of a country's economy is greatly influenced by global economic conditions, given the increasingly close links between countries through economic relations and international cooperation. One of the main factors in economic growth is international trade, particularly export and import activities. Crude oil is one of the most actively traded commodities. Given the highly volatile crude oil market, accurate price forecasts are crucial in economic and financial decision-making. This study compares the performance of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Fuzzy Time Series Markov Chain (FTSMC) in forecasting the price of West Texas Intermediate (WTI) crude oil using time series data from 2020 to 2024 with saturated sampling technique. The implementation of both methods is carried out through Matlab Online and R-Studio software, with results showing that ANFIS has higher accuracy than FTSMC, as evidenced by the Mean Absolute Percentage Error (MAPE) value of 1,8010% for ANFIS and 3,7567% for FTSMC. Further analysis shows that ANFIS with a triangular membership function as well as significant lags at lag 1, lag 3, lag 4, and lag 7 is able to produce more accurate predictions and match the trend of actual data. Therefore, ANFIS is recommended as a more effective method in forecasting WTI crude oil prices, which can provide valuable insights for policy makers and industry stakeholders.
Optimization of LightGBM Model with Bayesian Optimization for Malware Detection Kasim, Afrianto Pratama; Nasib, Salmun K.; Hasan, Isran K.; Wungguli, Djihad; Yahya, Nisky Imansyah
ILKOMNIKA Vol 7 No 1 (2025): Volume 7, Number 1, April 2025
Publisher : Lembaga Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28926/ilkomnika.v7i1.722

Abstract

Cyberattacks through malware on Android devices continue to rise, making accurate detection crucial. This research optimizes the LightGBM model using Bayesian Optimization to enhance accuracy and efficiency in detecting Android malware. A feature selection mechanism based on Attention Mechanism is applied to select the most relevant features for classification. The dataset used comes from the Canadian Institute for Cybersecurity (CIC) and consists of 17,804 Android applications, with a balanced distribution between malware and normal applications. The dataset is split into ratios of 80%:20%, 75%:25%, and 70%:30%. Feature selection reduces the number of features from 9503 to 300, 500, and 1000. The LightGBM model is then optimized with Bayesian Optimization to fine-tune parameters such as learning rate, number of iterations, and maximum number of leaves. The model's performance is evaluated using accuracy, precision, and recall metrics. Experimental results show that the model achieves 96,99% accuracy, 97,30% precision, and 96,70% recall with an 80%:20% dataset split and 1000 features. The combination of Attention Mechanism and Bayesian Optimization effectively improves processing efficiency without compromising performance.
Bilangan Terhubung Titik Pelangi pada Graf Garis, Graf Tengah, dan Graf Total dari Graf Panci Jusuf, Anryan; Nurwan, Nurwan; Yahya, Nisky Imansyah; Yahya, Lailany; Nasib, Salmun K.; Asriadi, Asriadi
Hexagon: Jurnal Ilmu dan Pendidikan Matematika Vol. 2 No. 2 (2024): October
Publisher : LPPM Universitas Terbuka

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33830/hexagon.v2i2.10000.

Abstract

Salah satu konsep dalam bidang teori graf yang berkaitan erat dengan pewarnaan titik pelangi adalah bilangan terhubung titik pelangi. Sebuah graf G dikatakan terhubung titik pelangi jika terdapat setidaknya satu jalur yang menghubungkan titik-titik dengan warna yang berbeda. Konsep ini mengacu pada jumlah minimum warna yang dibutuhkan untuk mewarnai sebuah graf G sehingga graf tersebut terhubung dengan titik pelangi dan dilambangkan dengan rvc(G). Topik pewarnaan titik pelangi dapat dieksplorasi dalam berbagai bentuk pengembangan graf dengan memanfaatkan graf garis, graf tengah dan graf total. Graf garis L(G) adalah graf yang titik-titiknya merupakan sisi-sisi dari G, dan jika u, v ≥ E(G) maka uv ≥ E(L(G)) sedemikian sehingga u dan v saling berbagi titik di G. Graf tengah M(G), merupakan sebuah graf dengan himpunan titiknya merupakan gabungan antara kumpulan titik dan kumpulan sisi dari graf G. Sedangkan graf total T(G), yaitu graf yang titiknya didapatkan dari himpunan titim dan himpunan sisi dari graf G, dimana setiap titik V(G)saling terhubung. Pada penelitian ini, saya membahas bilangan terhubung titik pelangi yang terhubung pada graf garis, graf tengah dan graf total dari graf panci (Pnm) dengan m ≥ 6. Berdasarkan hasil yang diperoleh, didapatkan teorema bilangan terhubung titik pelangi pada graf garis dari graf panci rvc(L(Pn_m)), graf tengah dari graf panci rvc(M(Pn_m)), dan graf total dari graf panci rvc(T(Pn_m)).
Bilangan Terhubung Pelangi pada Graf Tengah dari Graf Ilalang Rauf, Dewi Nur Angriani; Achmad, Novianita; Yahya, Nisky Imansyah; Nurwan, Nurwan; Nasib, Salmun K.; Asriadi, Asriadi
Jurnal Riset Mahasiswa Matematika Vol 4, No 4 (2025): Jurnal Riset Mahasiswa Matematika
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v4i4.31475

Abstract

Bilangan terhubung pelangi yang dinotasikan dengan adalah jumlah warna terkecil yang diperlukan untuk mewarnai sisi-sisi dari sebuah graf  sehingga graf tersebut menjadi terhubung pelangi. Bilangan terhubung pelangi dapat dipelajari dalam berbagai bentuk pengembangan graf yang dimodifikasi, termasuk graf tengah.  Semua jenis graf, baik yang sederhana maupun yang kompleks, dapat direpresentasikan sebagai graf tengah. Sebuah graf tengah yang dinotasikan dengan  dibentuk dari sebuah graf dan didefinisikan sebagai . Dua buah simpul pada  bersisian jika dan hanya jika keduanya bersisian dengan sebuah sisi pada , atau salah satu simpul pada  bersisian dengan sebuah sisi pada . Penelitian ini membahas tentang pewarnaan pelangi pada graf tengah dari graf ilalang  dengan  dan  Berdasarkan penelitian diperoleh teorema pelangi bilangan terhubung pada graf tengah graf ilalang untuk 
Penerapan Model ARFIMA-LSTM Menggunakan Variasi Estimasi Parameter Pembeda Dalam Meramalkan data IHPBI Harun, Trieke Nurfadilah; Djakaria, Ismail; Yahya, Nisky Imansyah; Nasib, Salmun K; Hasan, Isran K
Jurnal Riset Mahasiswa Matematika Vol 4, No 5 (2025): Jurnal Riset Mahasiswa Matematika
Publisher : Universitas Islam Negeri Maulana Malik Ibrahim Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/jrmm.v4i5.33303

Abstract

Indeks Harga Perdagangan Besar Indonesia (IHPBI) merupakan indikator penting dalam mengukur perkembangan ekonomi, khususnya pada sektor pertanian yang memiliki pengaruh besar terhadap daya beli masyarakat. Fluktuasi harga di sektor ini berdampak langsung pada kesejahteraan konsumen dan produsen, sehingga diperlukan metode peramalan yang akurat. Penelitian ini bertujuan untuk meramalkan IHPBI sektor pertanian menggunakan pendekatan hybrid Autoregressive Fractionally Integrated Moving Average (ARFIMA) dan Long Short-Term Memory (LSTM), serta membandingkan performa  metode estimasi parameter pembeda terbaik. Model ARFIMA digunakan untuk menangani komponen stasioner dan pola jangka panjang melalui diferensiasi pecahan, sedangkan LSTM digunakan untuk menangkap pola nonlinier dalam data. Keterbaruan dalam penelitian ini adalah membandingkan parameter pembeda terbaik yaitu Local Whittle dan Rescaled Range Statistics dalam hybrid ARFIMA-LSTM. Hasil dari penelitian yaitu peramalan menunjukkan tren naik IHPBI sektor pertanian selama 12 bulan ke depan. Metode estimasi parameter pembeda terbaik dalam model ARFIMA adalah Rescaled Range Statistics dengan nilai sebesar 0,322. Model hybrid ini menghasilkan nilai MAPE sebesar 0,6337853%, yang menunjukkan tingkat akurasi sangat tinggi.
Multicollinearity problem-solving with Jackknife Ridge Regression: A case study on slum conditions in Bone Bolango Adam, Dwi Putri Juniar; Nasib, Salmun K.; Adityaningrum, Amanda
Bulletin of Applied Mathematics and Mathematics Education Vol. 5 No. 1 (2025)
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/bamme.v5i1.12759

Abstract

Slum conditions in Indonesia, particularly in Gorontalo Province's Bone Bolango District, are a significant challenge to sustainable development. This research aims to identify the key factors contributing to slum conditions in the strategic economic areas of Kabila, Suwawa, and Tilongkabila using Jackknife Ridge Regression (JRR) analysis to address multicollinearity and overfitting issues. Data from the Regional Development Planning Board (BAPPEDA) Bone Bolango District's 2023 document was used, with a sample of 40 urban villages and villages. The result showed that there is a high collinearity between two independent variables, necessitating the use of JRR. The JRR model identified seven independent variables significantly related to slum value. The regression model explained 83% of the variability in slum conditions. This study provides methodological depth through the JRR framework, which enables accurate slum analysis where traditional models (like OLS) tend to fall short. It emphasizes the need for Bone Bolango to prioritize its policy initiatives by focusing on the seven independent variables. Additionally, the framework demonstrates scalability, making it adaptable to other Indonesian provinces that face similar challenges with slum data.
Pemodelan Multiple Discriminant Analysis Pada Perilaku Impulsive Buying Pengguna Shopee Pada Tanggal Cantik Naue, Siti Nurmeylisya; Yahya, Lailany; Abdussamad, Siti Nurmardia; Payu, Muhammad Rezky Friesta; Nasib, Salmun K
Griya Journal of Mathematics Education and Application Vol. 5 No. 2 (2025): Juni 2025
Publisher : Pendidikan Matematika FKIP Universitas Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29303/griya.v5i2.632

Abstract

Impulsive buying is a consumer behavior that makes purchases without prior planning. Impulsive buying is one of the consumer behavior phenomena that is increasingly widespread, especially on e‑commerce platforms such as Shopee during massive promotions on beautiful dates. Multiple Discriminant Analysis (MDA) is a method that has the advantage of classifying individuals into groups based on several predictor variables. The purpose of this study is to classify consumers into groups or categories based on their tendency to make impulse purchases on beautiful dates by analyzing the factors that influence them. The results of this study indicate that the beautiful date can affect the behavior of impulsive buying. The discriminant function model formed is able to distinguish categories of impulsive buying behavior with a good classification accuracy rate of 86.25% and the model shows that price is the most dominant or influential factor in distinguishing the category of impulsive buying of Shopee users on beautiful dates. This means that MDA Modeling is very helpful in classifying respondents into groups or categories based on the factors that influence them.
Rainbow vertex connection number and strong rainbow vertex connection number on slinky graph (SlnC4)) Akadji, Afifah Farhanah; Katili, Muhammad Rifai; Nasib, Salmun K.; Yahya, Nisky Imansyah
Desimal: Jurnal Matematika Vol. 4 No. 2 (2021): Desimal: Jurnal Matematika
Publisher : Universitas Islam Negeri Raden Intan Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24042/djm.v4i2.7276

Abstract

A graph is said rainbow connected if no path has more than one vertices of the same color inside. The minimum number of colors required to make a graph to be rainbow vertex-connected is called rainbow vertex connection-number and denoted by rvc(G) . Meanwhile, the minimum number of colors  required to make a graph to be strongly rainbow vertex-connected is called strong rainbow vertex connection-number and denoted by srvc(G). Suppose there is a simple, limited, and finite graph G. Thus, G=(V(G),E(G)) with the determination of k-coloring c:V(G)->{1,2,...,k} . The reaserch aims at determining rainbow vertex connection-number and strong rainbow vertex connection-number on slinky graphs (Sl_nC_4). Moreover, the research method applies a literature study with the following procedures; drawing slinky graphs (Sl_nC_4), looking for patterns of rainbow vertex connection-number, and strong rainbow vertex connection-number on slinky graphs (Sl_nC_4), then proving the theorems obtained from the previous pattern. It is obtained rvc(Sl_nC_4)=2n-1, srvc(Sl_2C_4)=4, and srvc(Sl_nC_4) = 3n-3 for n>= 3. 
RAINBOW CONNECTION NUMBER AND TOTAL RAINBOW CONNECTION NUMBER OF AMALGAMATION RESULTS DIAMOND GRAPH(〖Br〗_4) AND FAN GRAPH(F_3) Ismail, Sumarno; Hasan, Isran K.; Sigar, Tesya; Nasib, Salmun K.
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 16 No 1 (2022): BAREKENG: Jurnal Ilmu Matematika dan Terapan
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (980.35 KB) | DOI: 10.30598/barekengvol16iss1pp023-030

Abstract

If be a graph and edge coloring of G is a function , rainbow connection number is the minimum-k coloration of the rainbow on the edge of graph G and denoted by rc(G). Rainbow connection numbers can be applied to the result of operations on some special graphs, such as diamond graphs and fan graphs. Graph operation is a method used to obtain a new graph by combining two graphs. This study performed amalgamation operations to obtain rainbow connection numbers and rainbow-total-connection numbers in diamond graphs ( ) and fan graphs ( ) or . Based on the research, it is obtained that the rainbow-connection number theorem on the amalgamation result of the diamond graph ( ) and fan graph ( is with . Furthermore, the theorem related to the total rainbow-connection number on the amalgamation result of the diamond graph( ) and the fan graph ( is obtained, namely with .
RAINBOW VERTEX-CONNECTION NUMBER ON COMB PRODUCT OPERATION OF CYCLE GRAPH (C_4) AND COMPLETE BIPARTITE GRAPH (K_(3,N)) Yahya, Nisky Imansyah; Fatmawati, Ainun; Nurwan, Nurwan; Nasib, Salmun K
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 17 No 2 (2023): BAREKENG: Journal of Mathematics and Its Applications
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol17iss2pp0673-0684

Abstract

Rainbow vertex-connection number is the minimum colors assignment to the vertices of the graph, such that each vertex is connected by a path whose edges have distinct colors and is denoted by . The rainbow vertex connection number can be applied to graphs resulting from operations. One of the methods to create a new graph is to perform operations between two graphs. Thus, this research uses comb product operation to determine rainbow-vertex connection number resulting from comb product operation of cycle graph and complete bipartite graph & . The research finding obtains the theorem of rainbow vertex-connection number at the graph of for while the theorem of rainbow vertex-connection number at the graph of for for .
Co-Authors Abas Kaluku Abdul Wahab Abdullah Adam, Dwi Putri Juniar Afifah Farhanah Akadji Agusyarif Rezka Nuha Ainun Sukmawati Al Idrus Akadji, Afifah Farhanah Al Idrus, Ainun Sukmawati Armayani Arsal Asriadi Asriadi Asriadi Asriadi Biduli, Meiske Cindy Aisa Putri Noor Dewi Rahmawaty Isa Djafar, Fitria Djihad Wungguli Fadilah Istiqomah Pammus Fatmawati, Ainun Firdaus, Novarianti Gani, Friansyah Hamani, Mohamad Taufik Harun, Trieke Nurfadilah Hasan S. Panigoro Hasan, Riyanto Hinelo, Ikrar Prasetyo I Wayan Can Aryasandi Imran, Nurain Indrawati Lihawa Isa, Jefri N. Ismail Djakaria Isran K Hasan Jusuf, Anryan Kasim, Afrianto Pratama Kiayi, Fuji Fauzia La Ode Nashar Lailany Yahya Lakisa, Narti Macmud, Tedy Madonsa, Muhammad Rifai Mahmud, Sri Lestari Mahrifat Ismail Marukai, Nur Amalia Mokodompit, Marcela Muhammad Rifai Katili Musa, Adinda Pratiwi Nadiyyah, Ana Naue, Siti Nurmeylisya Nikmatisni Arsad Ningsih, Setia NISKY IMANSYAH YAHYA Nurwan Nurwan Nurwan Nurwan Nurwan Nurwan, Nurwan Pomahiya, Saiful Putri Ayuningtias Mahdang Rahmi, Emli Rakhmat Jaya Lahay Rauf, Dewi Nur Angriani Resmawan Resmawan Sembiring, Rinawati Sigar, Tesya Siti Nurmardia Abdussamad Stella Junus Sumarno Ismail Syahrizal Koem Taha, Dennynatalis Tiarawati, Ni Wayan Usman, Rizal Wahab, Nur Dhea wolah, Meitasya Yanuari, Eka Dicky D Zian Bula