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All Journal Journal of System and Computer Engineering Madani: Multidisciplinary Scientific Journal Sentra Dedikasi: Jurnal Pengabdian Masyarakat (JUMPER) Journal Pharmacy and Application of Computer Sciences Advances in Computer System Innovation Journal (ACSI Journal) Indonesian Journal of Innovation Multidisipliner Research
Iskandar, Imran
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Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Medicine & Pharmacology Physics Public Health Social Sciences Other

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CNN Modeling for Classification of Bugis Traditional Cakes Iskandar, Imran; Jeffry, Jeffry; Fadliana, Nurul; Rimalia, Watty; Ahyana, Nurul
Journal of System and Computer Engineering Vol 6 No 1 (2025): JSCE: January 2025
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v6i1.1685

Abstract

Abstract This research aims to create a classification system that can recognize traditional Bugis cakes using the Convolutional Neural Network method. (CNN). Traditional Bugis cakes play an important role in Indonesia's culinary heritage, which is rich in diversity and flavor. However, the lack of documentation and sufficient recognition of these cakes could lead to the loss of cultural knowledge. In this study, a collection of images of traditional Bugis cakes was gathered and processed for training a CNN model. This model was created to recognize and classify various types of cakes based on their visual attributes. The evaluation results show that the CNN model can achieve a high level of accuracy in identifying these cakes, making it a useful tool in preserving and promoting traditional Bugis cakes. This research is expected to contribute to the development of image recognition technology and raise public awareness about the richness of local culinary heritage. Keywords : Convolutional Neural Network (CNN), Bugis Cake, Indonesian Cuisine
ANALISIS KEPUASAN PASIEN RAWAT JALAN MENGGUNAKAN KAISER MEYER OLKIN UNTUK PEMBELIAN OBAT KEMBALI DI RSUD KOTA MAKASSAR Iskandar, Imran; Ishak, Pertiwi
Journal Pharmacy and Application of Computer Sciences Vol. 1 No. 1: Februari: 2023: JOPACS
Publisher : Arlisaka Madani Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59823/jopacs.v1i1.14

Abstract

Pelayanan yang maksimal merupakan bagian yang perlu dipertimbangkan demi terbangunnya kepuasan pasien rumah sakit. Penelitian ini dilaksanakan dengan tujuan untuk mengetahui pengaruh kualitas pelayanan terhadap kepuasan pasien rawat jalan dalam pembelian obat pada RSUD kota Makassar. Untuk mengetahui pengaruh citra rumah sakit terhadap kepuasan pasien rawat jalan. Penelitian ini termasuk jenis penelitian kuantitatif. Data penelitian diperoleh dengan penyebaran kuisioner terhadap 40 orang pasien rawat jalan yang sedang berobat di rumah sakit. Data penelitian dilakukan uji Analisis factor dalam program aplikasi SPSS.
Pelatihan Panduan Dalam Pengoperasian Microsoft Office Bagi Perangkat Desa Iskandar, Imran; Nur, Nur Hamdani; Rimalia, Watty; Panggabean, Benny Leonard; Tenriana, Nuzul; Herianto, Dedy; Iriany, Rosary
SENTRA DEDIKASI: Jurnal Pengabdian Masyarakat Vol. 2 No. 2 (2024): Mei 2024
Publisher : Arlisaka Madani Group

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59823/dedikasi.v2i2.61

Abstract

Pelatihan Panduan Dalam Pengoperasian Microsoft Office bertujuan untuk meningkatkan keterampilan perangkat desa di Desa Lentu dalam menggunakan aplikasi perkantoran yang esensial. Program pelatihan ini melibatkan 20 peserta, termasuk kepala desa, sekretaris desa, bendahara, dan staf administrasi, yang sebelumnya memiliki keterbatasan dalam penguasaan Microsoft Word, Excel, dan PowerPoint. Metode pelatihan meliputi demonstrasi langsung, praktik individu, dan sesi tanya jawab, dengan fokus pada penerapan keterampilan dalam konteks administratif desa. Hasil evaluasi menunjukkan peningkatan signifikan dalam keterampilan peserta. Sebelum pelatihan, hanya 30% peserta yang memiliki pengetahuan dasar mengenai Microsoft Office. Setelah pelatihan, 85% peserta mampu menggunakan Microsoft Word untuk menyusun dokumen resmi dengan format yang benar, 80% peserta berhasil mengelola data keuangan menggunakan Microsoft Excel, dan 75% peserta dapat membuat presentasi efektif dengan Microsoft Power Point. Umpan balik dari peserta menunjukkan peningkatan kepercayaan diri dan kepuasan kerja, serta penerapan keterampilan yang lebih efisien dalam tugas-tugas administratif sehari-hari. Pelatihan ini juga menghasilkan modul panduan yang dapat digunakan sebagai referensi berkelanjutan bagi perangkat desa dan komunitas lain yang membutuhkan pelatihan serupa. Saran untuk tindak lanjut mencakup perpanjangan durasi pelatihan, pelatihan lanjutan, dan penyediaan dukungan teknis berkelanjutan. Temuan ini diharapkan dapat memberikan kontribusi pada peningkatan efektivitas administrasi desa dan pelayanan publik di masa depan.
Sistem Monitoring Status Meja Pada Restoran Berbasis Internet of Things (IOT) Mardewi, Mardewi; Iskandar, Imran; Sofyan, Sofyan; La Wungo, Supriyadi; Aziz, Firman
Journal of System and Computer Engineering Vol 4 No 2 (2023): JSCE: Juli 2023
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v4i2.816

Abstract

The condition of a busy restaurant sometimes makes it difficult for waiters to monitor and provide satisfactory service to customers. Customers must wave when they want to call waiters but feel ignored or not seen and will feel uncomfortable with the atmosphere and calm in the dining room. An Internet of Things (IOT) based desk status monitoring system is a concept that has the ability to transfer data over a network without the need for human-to-human interaction. This study proposes a table status monitoring system in IOT-based restaurants. This tool is made so that it can be applied to large rooms and crowded visitors. A device equipped with wireless communication to send data to the server, so that it can be monitored in real time. If the button on the tool is pressed, the system will send a signal to the relay to give a call sign warning to restaurant staff/waitresses. The results of this study indicate that status information from tables requesting service from waiters will respond to these service requests and help optimize service at restaurants so that they can satisfy their customers.
Perancangan Sistem Monitoring Kualitas Udara Ruangan Berbasis Internet of Things (IoT) Iskandar, Imran; Rimalia, Watty; jeffry, Jeffry; Panggabean, Benny Leonard Enrico
Journal of System and Computer Engineering Vol 5 No 1 (2024): JSCE: Januari 2024
Publisher : Universitas Pancasakti

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

Abstract

The purpose of the research is to monitor air quality in the room by using MQ2, Microcontroller NodeMCU equipped with ESP8266 wireless module by using MQTT protocol for Data Communication sensor node to server. System testing uses a black box testing approach.The type of research used in this study is experimental research with the approach of PPDIOO methodology. This research is done by conducting trials where mechanical and electronic design for hardware components designed to build tools using this sensor can work according to the objectives and target desired.The results of this research show that when the sensor detects the existence 150 of smoke, gas, carbon monoxide (CO) then the value of the ADC sensor will give a warning notification in the form of an alarm generated from the tone/tone of the alarm will be active when the sensor is actively reading the value of ADC with a tolerance above 150 ppm
RANCANG BANGUN SISTEM INFORMASI TUGAS AKHIR MENGGUNAKAN MODEL, VIEW DAN CONTROLLER Panggabean, Benny Leonardo Enrico; Watty Rimalia; Imran Iskandar; Pijai, Pijai
Advances in Computer System Innovation Journal Vol. 2 No. 1: April 2024, ACSI Journal
Publisher : Unit Publikasi Ilmiah Perkumpulan Intelektual Madani Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51577/acsijournal.v2i1.475

Abstract

Proses pelaksanaan mata kuliah tugas akhir di Program Studi Ilmu Komputer Universitas Pancasakti Makassar masih menggunakan sistem manual yaitu mahasiswa harus mendatangi Sekretaris Program Studi untuk menyerahkan berkas-berkas syarat pelaksanaan tugas akhir sehingga membutuhkan waktu yang relatif lama dan kurang efisien, selain itu karena sistem ini masih bersifat “paper based” Sekretaris Program Studi cukup kesulitan dalam mengorganisir berkas-berkas tugas akhir yang dapat mengakibatkan berkas tersebut terselip, rusak bahkan hilang dan juga cukup kesulitan dalam menjadwalkan kegiatan tugas akhir mahasiswa. Dalam penelitian ini akan dirancang sistem informasi tugas akhir yang dapat digunakan untuk mengatasi masalah-masalah tersebut. Hasil dari penelitian ini adalah sistem informasi tugas akhir mahasiswa pada Program Studi Ilmu Komputer Universitas Pancasakti Makassar. Dengan adanya sistem informasi tugas akhir mahasiswa ini, Sekretaris Program Studi selaku pihak yang mengelola kegiatan tugas akhir mahasiswa sangat terbantu sekali, karena sistem ini dapat meringankan dan mengurangi beban pekerjaan Sekretaris Program Studi yang dimana sekitar 50% beban pekerjaan tersebut sepenuhnya dilimpahkan kepada mahasiswa yang sedang melaksanakan tugas akhir.
KLASIFIKASI PENYAKIT MALARIA MENGGUNAKAN METODE CONVOLUTIONAL NEURAL NETWORK Aziz, Firman; Iskandar, Imran; Armansyah, M Rezky
Advances in Computer System Innovation Journal Vol. 2 No. 1: April 2024, ACSI Journal
Publisher : Unit Publikasi Ilmiah Perkumpulan Intelektual Madani Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51577/acsijournal.v2i1.518

Abstract

Penelitian ini mengembangkan model Convolutional Neural Network (CNN) untuk mendeteksi infeksi malaria dari gambar mikroskopis darah. Dataset "Cell Images for Malaria Detection" digunakan dan dibagi menjadi data pelatihan, validasi, dan pengujian, dengan augmentasi data melalui ImageDataGenerator untuk meningkatkan variasi dan kemampuan generalisasi model. Pelatihan model dilakukan dengan callback 'EarlyStopping' untuk menghindari overfitting dan mengoptimalkan waktu pelatihan, sehingga model berhenti lebih awal saat tidak ada peningkatan signifikan pada validasi loss. Hasil penelitian menunjukkan akurasi tinggi antara 0.9482 hingga 0.9595 dan nilai loss yang rendah, dengan konvergensi dalam 3 hingga 6 epoch. Evaluasi menggunakan dataset validasi memastikan bahwa model dapat memprediksi dengan akurat pada data yang belum pernah dilihat sebelumnya. Model ini menunjukkan potensi besar sebagai alat bantu diagnosis otomatis malaria yang cepat dan andal, terutama di daerah dengan keterbatasan sumber daya medis, sehingga dapat membantu mengurangi angka kematian dan morbiditas akibat malaria. Dengan demikian, penelitian ini memberikan kontribusi signifikan dalam upaya global untuk mengendalikan dan memberantas malaria.
Implementasi Algoritma Machine Learning untuk Forecasting Demand Pada Usaha Kerupuk Sehat Krusawi Wijaya, Neti Septi; Usman, Syahrul; Iskandar, Imran; Rimalia, Watty; Syam, Rahmat Fuady
Madani: Jurnal Ilmiah Multidisiplin Vol 4, No 1 (2026): February 2026
Publisher : Penerbit Yayasan Daarul Huda Kruengmane

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5281/zenodo.18358038

Abstract

The rapid development of information technology has encouraged business actors to utilize data analysis to improve efficiency and competitiveness, one of which is through demand forecasting. This study aims to implement machine learning algorithms to forecast product demand in the Krusawi Healthy Crackers business. The method employed is Prophet, which was selected due to its capability to handle time series data with nonlinear trends and seasonal patterns. The data used consist of historical daily sales data from April to July 2024, which were subsequently aggregated into weekly data. The research stages include data collection, data preprocessing (data aggregation, handling missing values, and Box-Cox transformation), Prophet model design with logistic growth and custom bi-monthly seasonality, model training, and performance evaluation. The results indicate that the Prophet model provides excellent forecasting performance, achieving a Mean Absolute Percentage Error (MAPE) of 6.57% or an accuracy level of 93.43%. The model successfully captures trend and seasonal patterns in Krusawi product sales. Therefore, the implementation of machine learning algorithms using the Prophet method proves to be a reliable solution for supporting production planning and inventory management in the Krusawi healthy crackers business, and has the potential to improve operational efficiency and business decision-making.
Machine Learning, Supervised Leveraging Supervised Machine Learning to Improve Accuracy in Predicting Smartphone Addiction Muhammad Nur Arafah; Imran Iskandar; Rahmat Fuadi Syam; Fitri Rahmadani; Mario Dendo; Serpasius Dappa Sudda
Indonesian Journal of Innovation Multidisipliner Research Vol. 4 No. 2 (2026): April - Juni
Publisher : Institute of Advanced Knowledge and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69693/ijim.v4i2.473

Abstract

The transformation of smartphones into essential life companions triggers new challenges in the form of digital addiction that erodes productivity and cognitive focus. In contrast to previous studies that focused on screen duration, this study explored a more critical dimension, namely the degradation of productive hours. The research aims to improve the prediction of addiction using supervised machine learning by integrating productivity variables as a core diagnostic feature. Through a database of 7,500 respondents, a comparative analysis was carried out on the Random Forest, K-Nearest Neighbors (KNN), and Naïve Bayes algorithms in The results of the study proved that the productivity variable significantly increased the predictability of the model. Random Forest consistently outperformed other algorithms across scenarios, with the highest accuracy reaching 93.32% at a 70:30 data ratio, surpassing KNN (89.66%) and Naïve Bayes (87.96%). With a specificity of 92.70%, these findings reveal that workflow disruption has a higher differentiating power than conventional duration metrics. In conclusion, productivity interference is a key indicator in identifying digital behavioral disorders. In practical terms, this research provides guidance for developers and managers in designing precise early warning systems to restore daily efficiency amid the distractions of the digital world.
The Role of Big Data Analytics, Digital Capability, and Agility on Firm Competitiveness in Indonesia Mardani Eka Ningrum; Nurul Ulya; Imran Iskandar; Muhammad Nur Arafah
Journal Management & Economics Review (JUMPER) Vol. 4 No. 1 (2026): July
Publisher : Malaqbi Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59971/jumper.v4i1.1233

Abstract

In this study, the impact of Big Data Analytics, Digital Capability, and Organizational Agility on Firm Competitiveness in Indonesia was analyzed. Being a part of the rapidly developing digital era, it becomes necessary for organizations to apply data-driven approaches, improve their digital competencies and develop agility to ensure competitiveness. A quantitative research design was used in the study. A cross-sectional survey involving 220 responses from managers and professionals was used in this case. The data obtained in the process of the survey were analyzed using the Structural Equation Modeling (SEM). It can be stated that each of Big Data Analytics, Digital Capability, and Organizational Agility impacts Firm Competitiveness positively and significantly. However, Digital Capability is the most influential variable. Organizational Agility has more impact on Firm Competitiveness than Big Data Analytics. Thus, the studied variables explain 68.3% of variance of Firm Competitiveness. Therefore, it can be stated that Firm Competitiveness in the digital era is determined by the application of technologies and organizational agility.
Co-Authors Arafah, Muhammad Nur Armansyah, M Rezky Fadliana, Nurul Firman Aziz Fitri Rahmadani Herianto, Dedy Iriany, Rosary Ishak, Pertiwi Jeffry Jeffry La Wungo, Supriyadi Mardani Eka Ningrum Mardewi, Mardewi Mario Dendo Nur Hamdani Nur Nurul Ahyana Nurul Ulya Panggabean, Benny Leonard Panggabean, Benny Leonard Enrico Panggabean, Benny Leonardo Enrico Pijai, Pijai Rahmat Fuadi Syam Rimalia, Watty Serpasius Dappa Sudda Sofyan Sofyan Syahrul Usman Syam, Rahmat Fuady Tenriana, Nuzul Watty Rimalia Wijaya, Neti Septi