Contoh pitch deck untuk presentasi ke investor dari morang moreng snack, pakarnya aneka keripik pedas yang pedasnya gak cabe cabean.
"isi pitch deck ini hanya contoh"
Contoh pitch deck untuk presentasi ke investor dari morang moreng snack, pakarnya aneka keripik pedas yang pedasnya gak cabe cabean.
"isi pitch deck ini hanya contoh"
Statistics assignment and homework help serviceTutor Help Desk
Looking for quality statistics assignment and homework help? Tutorhelpdesk offers you complete range of expert academic help for all grades of statistics projects at most realistic cost. We honor your timeline and are reachable 24x7 online at your service.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Essentials of Automations: Optimizing FME Workflows with Parameters
Forecast penjualan
1. BAB 3
FORECAST PENJUALAN
31 Pengertian Forecast Penjualan
Forecast Penjualan adalah suatu teknik proyeksi tentang tingkat permintaan konsumen
potensial pada suatu periode tertentu dengan menggunakan berbagai asumsi tertentu juga,
yakni sesuatunya berjalan seperti masa lalu. Dalam hal ini hasil dari suatu forecast lebih
merupakan pernyataan atau penilaian yang dikuantifisir terhadap kondisi masa depan
mengenai penjualan sebagai proyeksi teknis dari permintaan konsumen potensial untuk
jangka waktu tertentu. Meskipun demikian hasil perkiraan yang diperoleh mungkin saja tidak
sama dengan rencana. Hal ini disebabkan karena:
1. Forecast lebih merupakan pernyataan atau penilaian yang dikuantifisir terhadap
kondisi masa depan mengenai subjek tertentu, misalnya penjualan.
2. Forecast penjualan merupakan proyeksi teknis dari permintaan konsumen potensial
untuk jangka waktu tertentu, dengan menyebutkan asumsi yang mendasarinya.
3. Forecast selayaknya hanya dipandang sebagai bahan masukan untuk
mengembangkan suatu rencana penjualan.
4. Manajemen dapat menerima, memodifikasi atau menolak hasil dari suatu forecast.
32 Metode-metode yang digunakan
Metode forecast penjualan dapat dikelompokan sebagai berikut:
I.Judgmental Method atau Non Statistic Method, yakni metode memproyeksikan
penjualan yang berdasar pada pendapat selesman, sales manager, para ahli, dan survey
konsumen.
II.Statistical Method, meliputi:
A. Analisa Trend, yang terdiri dari:
1. Penerapan garis trend secara bebas
2. Penerapan garis trend dengan metode setengah rata-rata
3. Penerapan garis trend secara matematis, yang terbagi
1
2. menjadi:
a. Metode Moment
b. Metode Kuadrat terkecil (Least Square)
c. Metode Kuadrat (garis lengkung)
B. Analisa Korelasi
III.Specific Purpose Method, meliputi:
A. Analisa Industri
B. Analisa Product Line
C. Analisa Penggunaan Akhir
Faktor-faktor yang mempengaruhi pemilihan cara pembuatan forecast penjualan, antara
lain:
a. Sifat produk
b. Metode distribusi
c. Luas lahan
d. Persaingan
e. Data historis yang tersedia
Beberapa kebijaksanaan yang dipengaruhi oleh hasil forecast penjualan antara lain:
a. Kebijaksanaan perencanaan produksi
b. Kebijaksanaan persediaan
c. Kebijaksanaan pemakaian mesin
d. Kebijaksanaan investasi aktiva tetap
e. Kebijaksanaan pembelian bahan baku dan bakan pembantu
f. Kebijaksanaan aliran kas
3. Perusahaan Rokok DJARUM
Data Penjualan selama 8 tahun terakhir
Tahun Penjualan Tahun Penjualan
1995 8.000 1999 10.400
1996 8.800 2000 10.800
1997 10.000 2001 12.000
1998 9.200 2002 12.400
Y = a + bx
A. Metode Least Square (Kuadrat terkecil)
Parameter X disusun dan di usahakan agar jumlahnya sama dengan nol ( ∑X = 0 )
Tahun (n) Penjualan (Y) X X2 XY
1995 8.000 -7 49 - 56.000
1996 8.800 -5 25 - 44.000
1997 10.000 -3 9 - 30.000
1998 9.200 -1 1 - 9.200
1999 10.400 1 1 10.400
2000 10.800 3 9 32.400
2001 12.000 5 25 60.000
2002 12.400 7 49 86.000
Jumlah ( ∑ ) 81.600 0 168 50.400
Rumus:
Y = a + bX
= 10.200 + 300 X
Y 2003 = 10.200 + 300 (9)
= 12.900
3
4. B. Metode Moment
Tahun (n) Penjualan (Y) X X2 XY
1995 8.000 0 0 0
1996 8.800 1 1 8.800
1997 10.000 2 4 20.000
1998 9.200 3 9 27.600
1999 10.400 4 16 41.600
2000 10.800 5 25 54.000
2001 12.000 6 36 72.000
2002 12.400 7 49 86.000
Jumlah ( ∑ ) 81.600 28 140 310.800
81.600 = 8a + 28b (x 5) 408.000 = 40a + 140b
310.800 = 28a + 140b (x 1) 310.800 = 28a + 140b
97.200 = 12a
a = 8.100
∑Y = a.n + b.∑X Rumus: Y = a + bX
81.600 = 8.100 (8) + 28b Y = 8.100 + 600 X
= 64.800 + 28b Y 2003 = 8.100 + 600 (8)
b = 600 Y = 12.900
∑Y = Jumlah data historis
n = Jumlah waktu data
x = Nilai pada setiap periode waktu
a = Nilai Y pada titik 0
b = Lereng garis lurus
C. Metode setengah rata-rata (semi average)
Tahun (n) Penjualan (Y) X
5. 1995 8.000 -3
1996 8.800 -1
1997 10.000 1
1998 9.200 3
1999 10.400 5
2000 10.800 7
2001 12.000 9
2002 12.400 11
2003 a a a 13
a = Rata-rata kelompok 1
b =
n = Jarak waktu antara dengan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a
3c959adb57529070d379fefb0d80c5144c0481e94c118d180e755af4ab54b865888e90dc83fd5
3b999d15799894c1862e45f9b28e292f8aaa07f1590c7898c573065578e989f2c285841d5e777
1ba8b0ff4e576bb9be1a8eee001d43f3372d3e533f20000000049454e44ae4260820000000840
010824000000180000000210c0db01000000030000000000000000000000000000001b40000
040000000340000000100000002000000000000bf000000bf00006041000088410300000000
0000b3000000b3ffff5f41000000b3000000b3ffff87412100000008000000620000000c000000
01000000150000000c00000004000000150000000c000000040000005100000000020000000
00000000000000d00000010000000000000000000000000000000000000000e000000110000
0050000000a0000000f000000010010000000000002000cc000e00000011000000280000000e
0000001100000001000800000000000000000000000000000000001e0000000000000000000
000ffffff0090dbff0000003a00903a0000ffffdb00b6ffff0000006600b6660000dbffff009066900
0b6ffdb003a006600dbdb9000663a9000ffffb600003a90003a000000ffdb90003a90db0066b6ff
00903a3a00903a660090dbdb003a003a006666b6000066b60066000000ffb66600db903a0001
01010101010101010203000000ffff01010101010101010101131d0114ffff010101010101010
1010101131d01ffb61a1b0f0910001c0101010101131c0000010616171812190f01010101090e
ffff0101020304050101010215050607ffff0101011308010101010114000004ffff01010910111
20101010101010101ffff090a0b0c0d0e0f01010101010101ffff0102030405060708010101010
10166b60101010101010101010101010101ffff0101010101010101010101010101ffff010101
5
6. X = Jumlah tahun dihitung dari periode dasar (nilai pada setiap periode waktu)
Rumus: Y = a + bx
Y = 9.000 + 300x
Y 2003 = 9.000 + 300 (13)
= 12.900
7. D. Forecast berdasarkan metode khusus
1) Analisis Industri
Dalam analisis ini lebih ditekankan pada “ Market Share “ yang dimiliki
perusahaan. Analisis ini menghubungkan potensi penjualan perusahaan dengan
industri pada umumnya (volume, posisi dalam persaingan).
Tahapan dalam pemakaian analisis industri:
a. Membuat proyeksi permintaan industri
b. Menilai posisi perusahaan dalam persaingan
2) Analisis product line
Umumnya analisis product line digunakan pada perusahaan yang menghasilkan
beberapa macam produk yang tidak mempunyai kesamaan, sehingga dalam
membuat forecastnya harus terpisah.
3) Analisis penggunaan akhir
Bagi perusahaan yang menghasilkan produk setengah jadi, masih memerlukan
proses lebih lanjut menjadi produk jadi dan siap untuk dikonsumsi, maka dalam
pembuatan forecastnya ditentukan oleh penggunaan yang ada kaitannya dengan
produk yang dihasilkan.
33 Pemilihan Metode Terbaik Dalam Peramalan
Garis Lurus
Tahun (n) Penjualan (Y) X X2 XY
2001 130 -2 4 - 260
2002 145 -1 1 - 145
2003 150 0 0 0
2004 165 1 1 165
2005 170 2 4 340
760 0 10 100
7
25. 2005 170 172 170.71
Dari tabel penjualan nyata dan ramalan penjualan, bila metode trend garis lurus dibandingkan
dengan metode trend garis lengkung tampak ramalan penjualan metode garis lengkung lebih
mendekati penjualan nyata. Karena itu metode trend garis lengkung lebih tepat digunakan
untuk membuat ramalan penjualan tahun 2006. tetapi karena ramalan penjualan metode garis
lengkung tidak persis sama dengan penjualan nyata maka sebaiknya untuk membentuk
metode mana yang paling baik, digunakan standar kesalahan peramalan (SKP) dengan rumus:
X = Penjualan nyata
Y = Ramalan penjualan
n = banyaknya data yang paling sesuai
Trend Garis Lurus
Tahun X Y (X-Y) (X-Y)2
2001 130 132 -2 4
2002 145 142 3 9
2003 150 152 -2 2
2004 165 162 3 9
2005 170 172 -2 4
Jumlah 28
SKP = 2.36
Trend Garis Lengkung
Tahun X Y (X-Y) (X-Y)2
2001 130 130.56 - 0.56 0.312
2002 145 142.71 2.29 5.244
2003 150 153.42 - 3.42 11.70
2004 165 162.71 2.29 5.244
2005 170 170.71 - 0.71 0.504
Jumlah 23
SKP = 2.15
25
26. Karena SKP trend garis lengkung sebesar 2.15 lebih kecil daripada SKP trend garis lurus
sebesar 2.36 maka metode trend garis lengkung lebih sesuai untuk ramalan penjualan tahun
2006.