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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Media Statistika Jurnal Studi Manajemen Organisasi Elkom: Jurnal Elektronika dan Komputer Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmiah KOMPUTASI BAREKENG: Jurnal Ilmu Matematika dan Terapan JOURNAL OF APPLIED INFORMATICS AND COMPUTING JTAM (Jurnal Teori dan Aplikasi Matematika) Jiko (Jurnal Informatika dan komputer) JURNAL PENDIDIKAN TAMBUSAI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Jurnal Pendidikan dan Konseling bit-Tech JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Pembelajaran Pemberdayaan Masyarakat (JP2M) International Journal of Advances in Data and Information Systems Al-Mutharahah: Jurnal Penelitian dan Kajian Sosial Keagamaan Studies in Learning and Teaching Jurnal Lebesgue : Jurnal Ilmiah Pendidikan Matematika, Matematika dan Statistika Nusantara Science and Technology Proceedings Jurnal Teknik Informatika (JUTIF) Jurnal Bisnis Indonesia Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) International Journal of Data Science, Engineering, and Analytics (IJDASEA) Jurnal Kolaboratif Sains Al Khidma: Jurnal Pengabdian Masyarakat Jurnal Ilmiah Edutic : Pendidikan dan Informatika Malcom: Indonesian Journal of Machine Learning and Computer Science Eksponensial STATISTIKA Kohesi: Jurnal Sains dan Teknologi Information Technology International Journal (ITIJ) Seminar Nasional Teknologi dan Multidisiplin Ilmu Parameter: Jurnal Matematika, Statistika dan Terapannya Jurnal ilmiah teknologi informasi Asia RAGAM: Journal of Statistics and Its Application Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat)
Trimono, Trimono
Universitas Pembangunan Nasional Veteran Jawa Timur
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Physics Public Health Social Sciences Transportation Other

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Integrating IndoBERTweet and GRU for Opinion Classification on X Towards Public Transportation in Jakarta Nafiah, Fajria Ulumin; Panglima, Talitha Fujisai; Idhom, Mohammad; Trimono, Trimono
Journal of Applied Informatics and Computing Vol. 9 No. 5 (2025): October 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i5.10723

Abstract

Jakarta, the capital of Indonesia, faces persistent challenges with its public transportation system due to rapid urbanization, increased use of private vehicles, and poor service quality. While social media platforms such as X (formerly Twitter) offer valuable insights into public opinion, their unstructured nature complicates analysis. This study uses deep learning models to categorize user sentiments into six labels that cover positive and negative aspects of comfort, safety, and punctuality. The results show that IndoBERTweet achieved the highest performance, with 95.43% accuracy and a macro F1-score of 0.9545. It also required the shortest training time, at six minutes and 30 seconds. IndoBERTweet+GRU followed closely behind with an accuracy of 94.62% and a macro F1-score of 0.9460 in six minutes and 50 seconds. This shows that adding a GRU layer provides competitive results, but does not surpass the baseline model. Error analysis revealed that, while the models performed well with explicit sentiments, the models struggled with implicit expressions, such as sarcasm and mixed opinions. These results demonstrate the potential of sentiment analysis in real-time monitoring systems, which could help policymakers identify urgent issues and support data-driven improvements in Jakarta’s urban transportation services.
STOCK PRICE PREDICTION IN INDONESIA USING EXTREME GRADIENT BOOSTING OPTIMIZED BY ADAPTIVE PARTICLE SWARM OPTIMIZATION Safira, Alya Mirza; Trimono, Trimono; Hindrayani, Kartika Maulida
MEDIA STATISTIKA Vol 18, No 1 (2025): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.18.1.105-115

Abstract

High volatility is a major problem in generating accurate predictions of stock prices. It also causes unstable predictions and increases the loss risk. Therefore, an adaptive prediction model that is able to adjust to dynamic data pattern changes is needed. This study aims to address these issues by developing an Extreme Gradient Boosting (XGBoost) model optimized using Adaptive Particle Swarm Optimization (APSO). XGBoost was chosen for its ability to handle nonlinear relationships and minimize overfitting, while APSO serves to adaptively adjust parameters to obtain the optimal combination of hyperparameters. The novelty of this research lies in the application of XGBoost-APSO integration in the context of stock price prediction in the Indonesian capital market, which is characterized by high volatility. The study was conducted using daily closing price data of PT Aneka Tambang Tbk (ANTM) shares from November 2020 to May 2025 to predict prices seven days ahead. The results show that the XGBoost-APSO model provides the best performance with a MAPE value of 0.2%, superior to XGBoost-PSO (2.58%) and standard XGBoost (2.91%). This approach effectively improves prediction accuracy and supports quick and accurate investment decision making, while contributing to the development of intelligent prediction systems in the Indonesian capital market.
Pengaruh Faktor Lingkungan Terhadap Distribusi Kasus DBD di Jakarta Selatan Menggunakan Pendekatan Geographically and Temporally Weighted Regression (GTWR): The Effect of Environmental Factors on the Distribution of Dangue Fever Cases in South Jakarta Using Geographically and Temporally Weighted Regression (GTWR) Approach Carissa, Savvy Prissy Amellia; Sugiarti, Nova Putri Dwi; Trimono, Trimono; Idhom, Mohammad
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 4 (2025): MALCOM October 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i4.2116

Abstract

Demam Berdarah Dengue (DBD) adalah penyakit endemis yang dipengaruhi oleh banyak faktor lingkungan dan memiliki pola penyebaran yang kompleks secara spasial dan temporal.  Dengan menggunakan pendekatan Geographically and Temporally Weighted Regression (GTWR), penelitian ini bertujuan untuk menganalisis distribusi kasus DBD di wilayah Jakarta Selatan. Suhu maksimum dan suhu minimum memiliki dampak positif yang konsisten terhadap peningkatan kasus DBD, menurut hasil penelitian.  Kinerja model GTWR ditunjukkan dengan nilai R-squared 0,5697 dan AIC 556,766. Visualisasi peta risiko mengidentifikasi wilayah seperti Jagakarsa, Cilandak, dan Mampang Prapatan sebagai daerah dengan risiko tinggi, dan pola musiman memperlihatkan peningkatan kasus pada awal hingga pertengahan tahun serta penurunan pada musim kemarau.
Peranan Baznas dalam Meningkatkan Perekonomian Masyarakat 3T di Kabupaten Kepulauan Meranti Provinsi Riau Nasution, Baktiar; Herlina, Herlina; Abdullah, Abdullah; Trimono, Trimono; Rafiqah, Lailan
Jurnal Pendidikan Tambusai Vol. 6 No. 3 (2022): December 2022
Publisher : LPPM Universitas Pahlawan Tuanku Tambusai, Riau, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/jptam.v6i3.14718

Abstract

Kesmiskinan merupakan persoalan krusial yang menjadi pusat perhatian pemerintah. Salah satu aspek yang terpenting dalam menanggulangi kemiskinan dengan adanya data Untuk melakukan pengukuran tingkat kemiskinan disetiap kabupaten kota diseluruh indonesia. Beradasarkan data dari badan pusat statistik provinsi riau kabupaten kepulauan meranti berada pada kategori tinggi untuk tingkat kemiskinan pada angka 25.68%. Tujuan penelitian ini adalah untuk Meningkatkan Perekonomian Masyarakat 3 T Di Kabupaten Kepulauan Meranti Provinsi Riau melalui program baznas. Peneliti menggunakan pendekatan kualitatif Deskriptif dengan jenis studi kasus untuk menjabarkan secara rinci sesuai keadaan dilapangan. Dengan adanya pemberdayaan masyarakat mengarah pada perubahan sosial ekonomi sehingga masyarakat mampu dan mempunyai pengetahuan serta keterampilan dalam menjalani kehidupan untuk meningkatkan pendapatan, memecahkan permasalahan yang dihadapi, dan mengembangkan cara untuk menjangkau sumber daya yang diperlukan.
Comparison of Elbow and Silhouette Methods in Optimizing K-Prototype Clustering for Customer Transactions Kuswardana, Dendy Arizki; Prasetya, Dwi Arman; Trimono, Trimono; Diyasa, I Gede Susrama Mas
EDUTIC Vol 12, No 1: 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/edutic.v12i1.29744

Abstract

This research presents a comparative analysis of the Elbow and Silhouette methods to identify the ideal number of clusters in applying the K-Prototypes algorithm for customer grouping using purchase transaction data. The K-Prototypes algorithm is employed due to its ability to handle both numerical and categorical data simultaneously. Customer purchase transaction data from the Point of Sale (POS) system is analyzed through preprocessing, feature transformation, and attribute segmentation stages before being clustered using the K-Prototypes algorithm. To identify the optimal cluster count, this study employs two methods: the Elbow and the Silhouette method. The results indicate that the Elbow method produces 2 clusters with a model evaluation score of 0.6368, while the Silhouette method suggests 2 clusters with a slightly lower score of 0.6186. In terms of computational efficiency, the Elbow method also demonstrates a faster processing time results highlight the significance of choosing an appropriate method for identifying the ideal number of clusters, ensuring it aligns with the specific goals of the analysis, whether emphasizing superior inter-cluster distinction or favoring a more parsimonious model configuration.
Tantangan Guru PAI Dalam Menghadapi Era Digital 5.0 Di Madrasah Aliyah Darul Ulum Pulau Kijang Indragiri Hilir Trimono, Trimono; Hasan; Bainar; Aisyah, Azni
Al-Mutharahah: Jurnal Penelitian dan Kajian Sosial Keagamaan Vol 22 No 02 (2025): Al-Mutharahah : Jurnal Penelitian dan Sosial Keagamaan
Publisher : LPPM Institut Agama Islam Diniyyah Pekanbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Abstract This study aims to examine the challenges faced by Islamic Education (PAI) teachers in adapting to the Digital Era 5.0 at MA Darul Ulum Pulau Kijang, Indragiri Hilir, Riau. The Digital Era 5.0 requires teachers to master information technology in the learning process, including planning, implementation, and evaluation. Based on field observations, it was found that some teachers still rely on traditional methods, lack skills in using digital devices, and face limitations in facilities and infrastructure. This research identifies four main challenges faced by PAI teachers: operating digital platforms such as PMM, designing technology-based lessons, creating interactive learning experiences, and using digital assessment tools. To overcome these challenges, teachers engage in self-directed learning, peer discussions, and participate in both online and offline training programs. The study concludes that improving teachers’ digital competence is essential to support the effectiveness of learning in the era of digital transformation. Keywords: Challenges, of PAI Teachers, Digital Era 5.0 Abstrak Penelitian ini bertujuan untuk mengkaji tantangan yang dihadapi guru Pendidikan Agama Islam (PAI) dalam menghadapi era digital 5.0 di MA Darul Ulum Pulau Kijang, Indragiri Hilir, Riau. Era digital 5.0 menuntut guru untuk mampu menguasai teknologi informasi dalam proses pembelajaran, baik dalam aspek perencanaan, pelaksanaan, hingga evaluasi. Berdasarkan studi lapangan, ditemukan bahwa sebagian guru masih menggunakan metode tradisional, kurang terampil dalam menggunakan perangkat digital, serta menghadapi keterbatasan sarana dan prasarana. Penelitian ini mengidentifikasi empat tantangan utama yang dihadapi guru PAI, yaitu keterampilan mengoperasikan platform digital seperti Platform Merdeka Mengajar (PMM), kemampuan merancang pembelajaran berbasis teknologi, menciptakan pengalaman belajar yang interaktif, dan penggunaan alat evaluasi digital. Untuk mengatasi hal tersebut, para guru melakukan berbagai upaya seperti pembelajaran mandiri, diskusi dengan rekan sejawat, dan mengikuti pelatihan baik daring maupun luring. Penelitian ini menyimpulkan bahwa peningkatan kompetensi digital guru sangat penting guna menunjang efektivitas pembelajaran di era transformasi digital. Kata Kunci: Tantangan, Guru PAI, era digital 5.0,
Analisis Pengaruh Tingkat Kriminalitas dan Kepadatan Penduduk Terhadap Indikator Kualitas Hidup Masyarakat melalui Pendekatan Two-Way MANOVA Dewi, Ni Luh Ayu Nariswari; Zalfa Assyadida, Azizah; Salma Namira, Alivia; Nasrudin, Muhammad; Trimono, Trimono
EKSPONENSIAL Vol. 16 No. 2 (2025): Jurnal Eksponensial
Publisher : Program Studi Statistika FMIPA Universitas Mulawarman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30872/6ssnd846

Abstract

Quality of a population life is shaped by various social and structural conditions, including socioeconomic disparities, crime levels, and population pressure. Understanding how these factors interact is essential for evaluating regional welfare. Therefore, this study aims to examine the influence of crime rates and population density on the quality of life in Indonesia using a Two-Way Multivariate Analysis of Variance (MANOVA) approach. The dependent variables analyzed include the Human Development Index (IPM), the percentage of the poor population, and the open unemployment rate. The independent variables consist of categories of crime rates and population density levels. Prior to conducting the MANOVA, assumption tests were performed to ensure data adequacy, including multivariate normality testing using Mardia’s test, independence testing via Bartlett's Test of Sphericity, and homogeneity of variance testing with Box’s M Test. The analysis results indicate that neither crime rates nor population density levels significantly influence the three quality of life indicators simultaneously, as evidenced by the Wilks’ Lambda and Pillai’s Trace test outcomes. These findings suggest that policies aimed at improving quality of life should not solely focus on crime rates and population density but require a multidimensional approach encompassing other factors such as education, healthcare, and economic conditions. 
Implementasi Spatial Durbin Model Berbasis Data Science Untuk Analisis Kemiskinan Jawa Timur Arif, Farah Yusnaida; Mohammad Idhom; Trimono, Trimono
Seminar Nasional Teknologi dan Multidisiplin Ilmu (SEMNASTEKMU) Vol. 5 No. 1 (2025): SEMNASTEKMU
Publisher : Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/9w9pye50

Abstract

Poverty remains a major development challenge that requires data-driven analysis to understand its variation across regions. This study focuses on East Java, where spatial interdependence is suspected to influence poverty distribution, making spatial analysis relevant for supporting regional policy design. The study examines determinants of poverty using the Spatial Durbin Model, which captures both direct effects and indirect spatial spillovers through lagged independent variables. The analytical workflow is implemented using a Python-based data science pipeline to ensure a systematic, transparent, and reproducible process, in line with current trends in technology-supported research. The dataset consists of 2024 secondary data from the Indonesian Central Bureau of Statistics. The analysis includes data preprocessing, construction of a Queen Contiguity spatial weight matrix, Moran’s I test to detect spatial autocorrelation, and SDM estimation. Results indicate significant positive spatial autocorrelation (I = 0.4099; p = 0.0008), showing that poverty is not randomly distributed. While the spatial lag of the dependent variable is not significant, an indirect spatial effect appears through the Gini Ratio (θ₄ = –39.42168; p = 0.03855). Moreover, the Human Development Index significantly reduces poverty. These findings highlight the roles of regional inequality and human development in shaping poverty dynamics and provide insights for more targeted policy interventions.
Prediksi Hasil Produksi Beras di Kabupaten Lamongan Menggunakan Stochastic Frontier Analysis (SFA) Imelda Widya Ningrum; Dwi Arman Prasetya; Trimono
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 7 No. 1 (2025): Juni 2025
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v7i1.15556

Abstract

The agricultural sector plays a crucial role in the Indonesian economy, especially in maintaining food estate and economic stability. This study aims to identify and improve the technical efficiency of rice production in Indonesia using Stochastic Frontier Analysis (SFA). Agricultural data were analyzed through validation, cleaning, feature selection, and modeling with a log-linear Cobb-Douglas production function estimated using Maximum Likelihood Estimation (MLE). Model performance was evaluated on training and test data using Log-likelihood, R-squared, and Mean Absolute Percentage Error (MAPE). The results showed good model prediction performance on test data (R-squared = 0.6658 and MAPE = 14.34%). Technical inefficiency analysis indicated that the level of inefficiency among farmers in Lamongan Regency was relatively low and homogeneous. However, the efficiency frontier analysis identified significant opportunities to increase rice yields through more efficient management of production factors and reduction of inefficiencies
Prediksi Harga Saham Menggunakan ARIMA Outlier sebagai Pendekatan Awal Menuju Analisis AI Keuangan Adam, Cindi; Idhom, Mohammad; Trimono, Trimono
Elkom: Jurnal Elektronika dan Komputer Vol. 18 No. 2 (2025): Desember : Jurnal Elektronika dan Komputer
Publisher : STEKOM PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/elkom.v18i2.3314

Abstract

Perkembangan kecerdasan buatan (AI) mendorong inovasi dalam analisis keuangan, termasuk prediksi harga saham yang fluktuatif. Penelitian ini bertujuan memprediksi harga saham PT Garudafood Putra Putri Jaya Tbk menggunakan model ARIMA dengan penanganan Outlier sebagai pendekatan awal menuju sistem prediksi yang lebih adaptif. Data harga penutupan harian dari Yahoo Finance dianalisis melalui uji stasioneritas, identifikasi model ARIMA, deteksi Outlier berbasis log-return, serta evaluasi performa menggunakan RMSE, MAE, dan MAPE. Hasil penelitian menunjukkan bahwa ARIMA Outlier memberikan performa lebih baik dibandingkan ARIMA dasar. ARIMA standar menghasilkan MAPE 1.32% dan AIC –899.46, sedangkan ARIMA dengan tiga dummy Outlier mencapai MAPE 1.16% dan AIC –900.37. Peramalan 14 hari ke depan menunjukkan pola yang stabil pada kisaran Rp 370–371. Pada data uji, ARIMA dasar memberikan akurasi terbaik pada pertengahan Agustus, sedangkan ARIMA Outlier mencapai akurasi tertinggi pada akhir Agustus dengan prediksi Rp 370.2 yang sangat dekat dengan harga aktual Rp 370.4. Hasil ini menunjukkan bahwa penanganan Outlier meningkatkan ketepatan model, sehingga ARIMA Outlier dapat digunakan sebagai fondasi awal menuju pengembangan sistem prediksi keuangan berbasis AI.
Co-Authors Abda Abda Abdullah Abdullah Adam, Cindi Adelia Adelia, Adelia Adiwidyatma, Afdhal Reshanda Afidria, Zulfa Febi Amanillah, Rahmatul Amri Muhaimin Andreas Nugroho Sihananto Ardiani, Ardia Eva Arif, Farah Yusnaida Arifta, Septia Dini Arrum Marwani Aurelia, Cenditya Ayu Aviolla Terza Damaliana Aviolla Terza Damaliana Aviolla Terza Damaliana Awang, Wan Suryani Wan Azni Aisyah Azzahra, Adelia Ramadhina Bagus Widduro Bainar Bainar, Bainar Bey Lirna, Cagiva Chaedar Carissa, Savvy Prissy Amellia Cindi Adam Damaliana, Aviolla Terza Desy Miftachul Ilmi Arifin Putri Dewi, Ni Luh Ayu Nariswari Di Asih I Maruddani Di Asih I Maruddani Di Asih I Maruddani Diash, Hakam Dzakwan Dinda Putri Arnindi Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Dwi Arman Prasetya Dwi Arman Prasetya Dwi Arman Prasetya Edi Sugiyanto Eny Widayawati Erna Novita Anggie Fahrudin, Tresna Maulana Fairuz Luthfia Winoto Putri, Maretta Farkhan Febri Giantara Febriyanti, Alvi Yuana Febyanti, Iin Hadi, Surjo Hadiyan Pradipta, Alvino Hasan Hendri Prabowo Herlina Herlina Hervrizal, Hervrizal I Gede Susrama Mas Diyasa I Gede Susrama Mas Diyasa I Gusti Putu Asto Buditjahjanto idhom, Mohammad Ikaningtyas, Maharani Ikaningtyas, Maharani Ilil Musyarof Asfiani Imanta Ginting Imelda Widya Ningrum Indira Zein Rizqin Insania, Nichlata Irawan, Tanaya Anindita Irma Amanda Putri Jacinda Ardina Gestyaki Kartika Maulida Hindrayani Kassim, Anuar bin Mohamed Khairunisa, Adenda Khosyi, Hanun Aufa Nur Kusdani, Kusdani Kuswardana, Dendy Arizki Linggasari, Dienna Eries Lisanthoni, Angela M Zufar Irhab S Putra Maharani Ikaningtyas Maruddani, Di Asih Mas'ad Mas'ad Maulana Pasha, Naufal Ricko Maulidiyyah, Nova Auliyatul Milla Akbarany Baktiar Putri Mochammad Abudrrochman Faiz Mohammad Idhom Mohammad Idhom Mohammad Idhom Muhaimin, Amri Muhammad Muharrom Al Haromainy Muhammad Nasrudin Muhammad Nasrudin Munoto Nabila, Nasywa Azzah Nabilah Selayanti Nafiah, Fajria Ulumin Nariyana, Calvien Danny Nasution, Baktiar Nathania, Vannesa Nevia Desinta Putri Ningrum, Imelda Widya Nova Auliyatul Maulidiyyah Novita Anggraini Nugraheni, Setiawati Oktaviani, Sheny Eka Panglima, Talitha Fujisai Prisma Hardi Aji Riyantoko Prismahardi Aji Riyantoko Putri, Irma Amanda Rafiqah, Lailan Rafli Feandika Nugroho, Muhammad Renaldi, Sahat Rhomaningtias, Lina Riswanda, Mohammad Nizar Ryan Dana, Alvin Sabela, Sefilah Naurah Safira Devi, Arsita Safira, Alya Mirza Salma Namira, Alivia Sekar Arum Melati Selly Rizkiyah Shindi Shella May Wara Sonhaji, Abdulah Sugiarti, Nova Putri Dwi Suprapto, Rheinka Elyana Susrama Mas Diyasa , I Gede Syamsul Rizal Tarno Tarno Taufik, Ikbar Athallah Terza Damaliana, Aviolla Tiara Audrey Anugerah Hadin Tresna Maulana Fahrudin Utami, Rianti Siswi Utriweni Mukhaiyar Valentina, Tiara Wahyu Syaifullah Jauharis Saputra Wardah, Salsabila Wibowo, Muhammad Bagas Satrio Widayawati, Eny Widison, Daffin Tanjiro Yuciana Wilandari Zalfa Assyadida, Azizah